The project entailed weekly meetings between the students and the Data Science project lead and mentor to ensure guidance around project parameters and to create a sense of community and connectivity throughout. With a visit to our headquarters to attend one of our global town hall meetings and feel a sense of community, the community that we were founded on, 60 years ago!

At the conclusion of the project, the students shared back their findings internally with our tech and product organization and at our virtual hackathon. The article below highlights one of the team’s research around Image Generation with Stable Diffusion. Thank you to the students for their research and efforts, we enjoyed having you and look forward to welcoming future capstone students from Columbia again. Please read on to hear more about the project in their own words.

Betsey Corey, Director, Strategic Talent Programs & Partnerships

Image Generation with Stable Diffusion

One of our favorite parts of the WeightWatchers app is the recipe recommendation section, where one can browse 12,000+ delicious (and healthy) meals. Each recipe is accompanied by beautiful, high-resolution images, which are often professionally created by WW. We tried to generate such images through latent diffusion models by simply giving the model a prompt, for example, “pasta puttanesca”. The recent release and rapid progression of diffusion models inspired this project and we sought to use stable-diffusion-v2–1 to generate pictures that resembled professional images as closely as possible.

Implementing the model & initial results

Getting things set up and generating the first images is very straightforward. We found running the model on a local or cloud GPU to speed things up considerably, especially when generating many images. At its most basic implementation, stable-diffusion-v2–1 only needs a prompt with a random seed being generated every time the model is run.

When experimenting with different recipes and food items, the model generated pictures of widely differing quality. Some prompts, such as full meal names (e.g., “pasta puttanesca with basil, black olives and tomatoes”) worked considerably better than single foods (e.g., “an apple”). Further, since most models generate a random seed each time the model is run, the same prompt will lead to vastly different outputs. This makes a rigorous evaluation more challenging. Here are some initial examples of images we generated:

(results for “clean pink table with a white round plate of chopped salad with sweet red pepper garlic clove […] string beans in the middle”)

(results for “apple”)

While these results are not terrible, they are still far from the professional benchmark we are comparing them to. The most noticeable deficiency at this stage was the appearance of unwanted items, such as hands, fingers, or cutlery, appearing on pictures despite the fact that our prompts did not include them. This led us to explore options to constrain the model or further specify instructions.

Improving the output

After some research and quite a bit of trial and error, we implemented the following measures to improve the outputs. First, we fed a list of negative prompts to the model, i.e. items that should be excluded from appearing. In addition, we varied the degree of complexity or detail of the prompts. Simply prompting with “Apple” yields different results than “high-resolution, shiny, centered Apple”. We tried to systematically determine whether complexity and detail in the prompt improved the images. We varied the seed and complexity of prompts for a given food item and created numerous images. Below, results for “banana” and “olive oil” are shown. Note that the complexity scale is arbitrary and subjective. Low complexity is something like “banana” while level 5 complexity is more akin to: “DSLR food photograph of normally shaped croissant, in a circular blue plate, white napkin background, light coming from the side, natural lighting, masterpiece, 4K, 85mm f1.8”.

This analysis showed that depending on the food, differing complexity levels of the prompts yielded different image qualities. To generate a realistic Apple or Banana, giving details such as “DSLR food photograph” and “high-resolution” proved to be effective. For items such as olive oil or salt, simplicity in the prompt was key.

The last avenue we have so far pursued is to use ControlNet which allows one to input a sketch and prompt, based on which the model will output an SD-generated image. This allows us to specifically draw out the position and shape of foods, which helps to constrain the outputs. See below for an example:

Looking forward, we seek to test some further ideas for improved prompt quality, such as using models like BLIP, which generate a prompt given an image. By feeding very high-quality images to such a model, we might be able to gain insight into how to phrase the prompts better.

Lessons learned

While the images we are currently generating are still not quite where we want them to be, the work with stable diffusion has taught us some useful lessons:

• When coming up with prompts, no one size fits all. Depending on the food to be generated, the prompt needs to be customized in order to get the best results.
• Working with negative prompts makes life considerably easier! Especially when seeking to eliminate elements that keep on popping up.
• Things move incredibly fast and each day new models are being published and resources made available. Staying up to date on Reddit, Discord and Huggingface has proven incredibly helpful.

For inspiration, here are a few of the most recent images we generated:

Voice Controlled Food Tracking

Background

Developing a food-tracking habit is consistently shown to be the most important factor in weight loss. WW is constantly looking for ways to make food tracking easier and exploring the use of AI tools to create better food tracking experiences. In this capstone project, our team built a website where people can talk about the food they eat, and the website can extract the food entities from the audio, matching with the current WW database, and returning the points.

The current way to track food on the Weight Watcher app is to manually search for the food that you eat for every meal. However, this process could become time-consuming and inefficient. For instance, Sweetgreen is one popular option among current customers. However, when it comes to a customized bowl, it could easily contain more than 10 different ingredients. This kind of complex real-life scenario deters users from tracking food consistently and accurately when they need it most. Taking images of food is a standard industrial solution and also might be your first thought. However, images could encounter problems as well. A human or model may be able to identify a casserole from an image, but this does not provide enough information to determine how we should track its nutritional value, which can vary widely depending on the ingredients used.

On the other hand, talking is a low-friction activity. People love to talk about food and can describe a whole meal in a few seconds. We believe that voice is the lowest friction method to track food. We also believe that the latest advances in open source and multimodal AI models have put “voice-to-track” within reach. In the rest of the blog, we will introduce it to you in more detail.

App Structure

Let’s take a closer look at the app structure and tech stack. At a very high level, the app is mainly coded in Python. We utilized FastAPI, used state-of-the-art models from open source communities, which are mainly OpenAI and Hugging Face, and deployed with docker. For the front end, we used Streamlit, which is a free and open-source framework to rapidly build and share beautiful data science web apps.

To transcribe a user’s audio describing the food to text, we used the Whisper model from OpenAI. To extract food names from the transcribed text, we tried both food-based BERT and GPT API. Next, we encoded the found food names in embeddings with SBert and then compared their similarity with encoded food names in the WW database using FAISS. Finally, we return the most related food names in the database to the front end.

App Demo

The figure below is a screenshot of the user interface of our app. First, users would be asked to record their voice by clicking on the microphone icon. Once the recording is successful, users would click on the “Detect” button to initiate the audio transcription, food extraction and matching process. The final matchings along with their respective portions, units, and points would be returned to the user and the user would provide feedback on the accuracy of the results by either selecting “Yes!!” or “No, this is not what I ate!” to answer the question “Is this what you ate?”. If the latter is selected, the user could choose to manually type in what they ate in the text box and new food extractions as well as final matchings would be generated based on the input in the textbox. Given that the matching is correct, the user could then click on the drop down menu on the rightmost column to adjust the amount of food intake as well as the unit. In addition, we also allow users to choose OpenAI’s GPT 3.5 Turbo as an alternative for Food-Based BERT to perform food entity extraction. Users could switch to this option by selecting the “OpenAI GPT 3.5-turbo” button and inputting their OpenAI API key in the leftmost column. Overall, our clear and interactive user interface allows users to track their daily food intake in an efficient way and provides highly customized results.

User Expression Pattern Research

Having our model at hand, we also conducted research on the user side to gain a deeper understanding of how people would use our app. We collected 112 recordings of people describing what they ate on the Columbia University morningside campus and performed analysis on these data collected in an attempt to find patterns in user expressions. The insights we gained from these real-life user data are as follows:

• Insight 1: Users almost always use ‘a’ to specify a single item but they tend to use ‘some’ when they had more than one item rather than specifying the specific amount
• Insight 2: When people describe what they ate, the name of the restaurant/ store is often neglected
• Insight 3: “and” usually connects two food entities whereas “with” might connect two food entities (e.g. toast with tea) or might be used when the user wanted to provide a more detailed description of the ingredients inside a food (e.g. coffee with oat milk/ bagel with cream cheese)
• Insight 4: Nouns (mostly foods) occur most frequently in people’s descriptions, with an overall frequency of 0.24. Better prompts might reduce the percentage of redundant information such as adjectives and adverbs.

Results

The accuracy of food entity extraction was compared between OpenAI GPT method and Food-Based BERT method. Out of all 112 collected transcribed texts, the Food-Based BERT method correctly extracts all food entities from 56.8% of the sentences, while the GPT method’s performance is 81.1%. Across all sentences, the Food-Based BERT method correctly detects 76.6% of entities, compared to 93.4% using the GPT method.

Next Steps

Potential future improvements that could be made to our app include running Whisper model on GPU to increase speed, finding a better name entity recognition model in terms of accuracy and speed, and adding aesthetic elements to our user interface. The next step would be to integrate our model with the WW app to provide users with this time-saving alternative for food tracking.

Interested in joining the WW team? Check out the careers page to view technology job listings as well as open positions for other teams.

Data Science Capstone Projects was originally published in WW Tech Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.

The project entailed weekly meetings between the students and the Data Science project lead and mentor to ensure guidance around project parameters and to create a sense of community and connectivity throughout. With a visit to our headquarters to attend one of our global town hall meetings and feel a sense of community, the community that we were founded on, 60 years ago!

At the conclusion of the project, the students shared back their findings internally with our tech and product organization and at our virtual hackathon. The article below highlights one of the team’s research around Image Generation with Stable Diffusion. Thank you to the students for their research and efforts, we enjoyed having you and look forward to welcoming future capstone students from Columbia again. Please read on to hear more about the project in their own words.

Betsey Corey -Director, Strategic Talent Programs & Partnerships

One of our favorite parts of the WeightWatchers app is the recipe recommendation section, where one can browse 12,000+ delicious (and healthy) meals. Each recipe is accompanied by beautiful, high-resolution images, which are often professionally created by WW. We tried to generate such images through latent diffusion models by simply giving the model a prompt, for example, “pasta puttanesca”. The recent release and rapid progression of diffusion models inspired this project and we sought to use stable-diffusion-v2–1 to generate pictures that resembled professional images as closely as possible.

Implementing the model & initial results

Getting things set up and generating the first images is very straightforward. We found running the model on a local or cloud GPU to speed things up considerably, especially when generating many images. At its most basic implementation, stable-diffusion-v2–1 only needs a prompt with a random seed being generated every time the model is run.

When experimenting with different recipes and food items, the model generated pictures of widely differing quality. Some prompts, such as full meal names (e.g., “pasta puttanesca with basil, black olives and tomatoes”) worked considerably better than single foods (e.g., “an apple”). Further, since most models generate a random seed each time the model is run, the same prompt will lead to vastly different outputs. This makes a rigorous evaluation more challenging. Here are some initial examples of images we generated:

(results for “clean pink table with a white round plate of chopped salad with sweet red pepper garlic clove […] string beans in the middle”)

(results for “apple”)

While these results are not terrible, they are still far from the professional benchmark we are comparing them to. The most noticeable deficiency at this stage was the appearance of unwanted items, such as hands, fingers, or cutlery, appearing on pictures despite the fact that our prompts did not include them. This led us to explore options to constrain the model or further specify instructions.

Improving the output

After some research and quite a bit of trial and error, we implemented the following measures to improve the outputs. First, we fed a list of negative prompts to the model, i.e. items that should be excluded from appearing. In addition, we varied the degree of complexity or detail of the prompts. Simply prompting with “Apple” yields different results than “high-resolution, shiny, centered Apple”. We tried to systematically determine whether complexity and detail in the prompt improved the images. We varied the seed and complexity of prompts for a given food item and created numerous images. Below, results for “banana” and “olive oil” are shown. Note that the complexity scale is arbitrary and subjective. Low complexity is something like “banana” while level 5 complexity is more akin to: “DSLR food photograph of normally shaped croissant, in a circular blue plate, white napkin background, light coming from the side, natural lighting, masterpiece, 4K, 85mm f1.8”.

This analysis showed that depending on the food, differing complexity levels of the prompts yielded different image qualities. To generate a realistic Apple or Banana, giving details such as “DSLR food photograph” and “high-resolution” proved to be effective. For items such as olive oil or salt, simplicity in the prompt was key.

The last avenue we have so far pursued is to use ControlNet which allows one to input a sketch and prompt, based on which the model will output an SD-generated image. This allows us to specifically draw out the position and shape of foods, which helps to constrain the outputs. See below for an example:

Looking forward, we seek to test some further ideas for improved prompt quality, such as using models like BLIP, which generate a prompt given an image. By feeding very high-quality images to such a model, we might be able to gain insight into how to phrase the prompts better.

Lessons learned

While the images we are currently generating are still not quite where we want them to be, the work with stable diffusion has taught us some useful lessons:

• When coming up with prompts, no one size fits all. Depending on the food to be generated, the prompt needs to be customized in order to get the best results.
• Working with negative prompts makes life considerably easier! Especially when seeking to eliminate elements that keep on popping up.
• Things move incredibly fast and each day new models are being published and resources made available. Staying up to date on Reddit, Discord and Huggingface has proven incredibly helpful.

For inspiration, here are a few of the most recent images we generated:

Interested in joining the WW team? Check out the careers page to view technology job listings as well as open positions for other teams.

Betsey Corey, Director, Strategic Talent Programs & Partnerships

Personally, I believe that one of the best parts of the WeightWatchers program is the concept of ZeroPoint® foods. These are foods that members can eat without tracking them or deducting any Points® from their daily Points Budget, giving them even more flexibility in what they choose to eat in a day. Earlier this year, WeightWatchers enabled flexible ZeroPoint categories, which adds logic in our food database for determining which foods should be valued at 0 Points.

This posed a complex problem. Each food has to be reviewed by the nutrition team to calculate its composition and be assigned to the right ZeroPoint foods category. Then a discount value is applied so that tracking these foods won’t deduct Points from a member’s daily Budget if the foods are in the member’s ZeroPoint foods list. For example, if “nonfat yogurt” is a ZeroPoint food for the member, then “vanilla nonfat yogurt” should also be considered as a ZeroPoint food and take no Point off for that member. In addition, this deduction should be applied even if the ZeroPoint food is only partially present in the food. For example, if avocado is a ZeroPoint food for the member, then the corresponding Points for avocado should be deducted from the total points of Chicken Avocado Salad.

Currently, we have millions of foods in our database. Manually evaluating each food and then assigning it to a category can be a huge undertaking. However, we hypothesized that this is the exact type of complex problem that is actually well suited for machine learning. We set out to discover if our predictions may be true by having Shuning, our data scientist intern, take this on as her summer research project. Our objective was to evaluate and confirm if we can solve this issue using machine learning tools or not. Shuning was able to solve this by using a combination of features and advanced techniques of stacking multiple machine learning models to create a proof of concept that worked really well. So what were the data, models, and results from Shuning’s research? Let’s break it down further:

Data

Data used in this project came from multiple sources. WeightWatchers has a presence globally, but to keep things manageable, this project focused on the U.S. market, where we collected a dataset of more than 50,000 foods. Variables used are food type, category, nutrition information, and ingredient statements.

Model

The four machine learning models used were decision tree, random forest, XGBoost, and LGBM. Prediction outputs from the four models, harmonic mean of the prediction outputs from all four models (harmean 4), and harmonic mean of the prediction outputs from random forest, XGBoost, and LGBM (harmean 3) are evaluated.

The metrics used for evaluation were R-squared, RMSE, and accuracy based on different residual thresholds (0.05, 0.1, 0.15, and 0.2). R-squared finds how much of the spread of the data can be explained by the features. The closer to one, the better it is. RMSE gives the difference between predicted value and actual value on average. The closer to zero, the better it is. The prediction output is considered accurate if the difference between actual value and predicted value is within a threshold level that allows for small errors in the estimation.

Results

Metrics are compared among different models and prediction outputs. Overall, harmean 3 gives the best result. In addition, two sets of variables are run — one without the ingredient statement and one with the ingredient statement. This is done to check if adding ingredient statements will improve the performance as this feature increases model complexity and places additional demands on data quality. It turned out that ingredient statements are very helpful. There are improvements on all metrics. See the below table for the corn category as an example.

To test model performance on new data, 55 foods were selected for testing. In general, the prediction outputs are good from an outsider perspective, even though it can sometimes underestimate the percentage of ZeroPoint foods within a dish. But since there are many rules behind food adjustment, further evaluation by the nutrition team is needed.

Next steps

This research project has proven that the problem is solvable. The next step is to make modifications and put them into production.

Shuning Yang — Data Science Intern

Interested in joining the WW team? Check out our careers page to view technology job listings as well as open positions on other teams.

Predicting food composition using machine learning was originally published in WW Tech Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.

While we foster innovation in our work everyday, we have a few extra fun ways we like to innovate as well. We created a slack channel called #WW-Hacks, where our engineers post ideas they are interested in hacking together and then work with management to give them the space to execute on those ideas — year round. Another is our annual Hackathon, which gives our teams the opportunity to think outside of the box and showcase their ideas. #WW-Hacks and our Hackathon have been so successful this year that we are already in the process of pushing some of the ideas to our members. In this post we are giving a sneak peek of how the Hackathon came to life and showcasing the amazing work from the participating teams.

We became a remote-first company in 2021 with a “work from wherever” philosophy, which meant our Hackathon needed to be reformatted to a virtual-friendly setting to ensure the entire company could attend. Some teams met in the office while others joined on Zoom.

Teams presented their projects before a panel of judges such as WeightWatchers’ CEO, Chief Technology Officer, and Head of Product to name a few, along with WeightWatchers employees across the company to experience creativity brought to life. The teams worked cross functionally and flipped their usual day-to-day roles which added a twist and an exciting challenge. It was a huge success, check out some of what they did….

• An exercise app that uses computer vision to analyze the effectiveness of your workouts.

• Suggesting personalized food pairings based on your food tracking history
• Personalizing the food search and recipe browsing experiences to meet members’ dietary preferences
• Building a new feature to enable members to share WW milestones to Instagram and other social networks.

Hack 22’ was a huge success and our members can expect to see some of the amazing work coming to life from each team live in our app soon!

At WeightWatchers we always enjoy taking the time to come together and maintain the strong foundation we are rooted in; a supportive community. To have fun, be creative and cheer one another on as we seek to provide the best digital experience possible for our members.

Betsey Corey - Director, Strategic Talent Programs & Partnerships

Interested in joining the WW team? Check out the careers page to view technology job listings as well as open positions on other teams.

Innovation at WeightWatchers! was originally published in WW Tech Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.

This summer, WW welcomed a class of interns across Tech and Product. Interns play a vital role in helping to drive our business forward and improve our digital offerings, while gaining career-enhancing experience that will position them well to enter the workforce after graduation.

This year’s experience was conducted virtually, so our interns represented WW from their homes in California, Georgia, Florida, North Carolina, Virginia, New Jersey, Connecticut, New York, New Hampshire, and Toronto! Normally, our interns would all sit near each other in our New York City, San Francisco, and Toronto offices. To help build that same camaraderie, I set up Zoom Pods and we kicked off each day as a team. The pods can be accessed at any time, allowing interns to get to know one another, support each other, collaborate on their projects, and come together as a group.

Throughout the eight-week program, interns took part in our Intern Speaker Series, hearing from external speakers, members of our Executive Committee, and other co-workers across WW. Interns participated in mock interview training, completed a Myers-Briggs Self Assessment, and learned how to build their personal brand. On Fridays, they attended a Virtual Workshop and had a chance to unwind together with a game night.

The virtual internship program ended up being a success, and we’re thankful to all of our interns for their dedication and enthusiasm. They will be missed! To wrap up the summer, we asked the interns to share a little bit about the program, in their own words. Take a read!

— Betsey Corey, Sr. Manager, Strategic Talent Programs & Partnerships

Meet WW’s latest interns!

This summer I interned as a front-end software engineer on the e-commerce shop team. I think the best part about the internship was how well WW integrates interns. The work I did mirrored that of the work the other engineers on my team did. This integration made for an incredible learning experience. What really made my summer was how much we interns were exposed to the business side of WW. This allowed me to develop a solid understanding of how exactly the work we do in engineering contributes to bringing WW’s vision of wellness to life. Another aspect of the internship that amazed me was how much freedom I had to network internally. I left almost every meeting with the impression that there’s truly no one at WW who isn’t open to answering the questions of curious interns. The people at WW create an incredible environment for learning and growth!

Devonte G. Duncan, Front-End Engineering Intern

I am an iOS Engineering Intern working with the Food Innovation / Onboarding team. It is very important to welcome new members to WW by preparing them for the journey ahead in an easy, seamless process. This summer, I was given the opportunity to improve the quality of that welcoming process. I was able to improve the usability of the app by allowing a wide range of screen sizes to view the same content without the need for scrolling, as well as update the visual design to make text and images easier to distinguish and read. Knowing that the work I have done helps people, makes it feel even more rewarding.

This was an exceptional learning experience. I was made to feel a part of the team, not like an intern. I never felt embarrassed by asking questions, I had the space to provide and receive meaningful feedback, and I was included and a part of discussions. I felt welcome. Something that I love about WW is the way that the working atmosphere parallels the member community. Members are given this space to talk about themselves, their hardships, their successes, their journey, without being met by criticism or judgment. It means a great deal to me that WW treats their members and employees with the same level of respect and appreciation.

Brooke Porter, iOS Engineering Intern

This summer I worked on the Marketing Technology team and learned about connecting WW user data to communications that engage members. It was my first time working within the Marketing Technology space so, every day was an opportunity to learn something new. I worked with Braze, a customer engagement platform used by WW, and gained exposure to architecture and syntax responsible for communications seen by members all over the world! Throughout the summer I was continuously supported by my team to take on different projects, ask questions, and was always treated as a member of the team! It was an amazing experience as well to work with different teams through this time as a remote intern. It has been a great internship experience that highlighted the evolution of WW.

Laisha Calalpa, Marketing Technology Intern

I worked as a Quality Engineering intern this summer on the Studio Tech team and the Wellness Pillars team. I am very thankful for the mentorship experience I was given while working with my coworkers on Front-end Automation testing. From day 1, the WW family made me a part of a team with the responsibility and opportunities as a full employee. The most enjoyable part of the internship program was interacting remotely with all the other interns. In particular, the MBTI session helped reveal how I can better approach diverse personalities in a work setting. The most rewarding part of this internship was seeing first-hand my impact and contributions in helping WW members on their personalized journeys.

Leah Meza, Quality Engineering Intern

This summer I had the immense pleasure of working with Michael D’Anvers and the quality engineering team as the accessibility education specialist intern. Throughout college, I had always either been a student teacher or a clinic shadow, so I was very nervous to work in a far more corporate environment than I was used to. However, from the moment I entered the first Zoom call, the sense of community within WW was almost tangible. From that moment on, I never felt like I was just an intern — I had been welcomed with open arms into the incredible WW community.

I was tasked with working on a few different projects. The first was a presentation on accessible tech for the interns. I was incredibly nervous about this as I don’t have a background in engineering or design, so I doubted my own authority in giving such advice. However, this presentation made me realize that accessibility is something that so many don’t know much about, and therefore simply opening people’s eyes to the field was incredibly impactful and I was met with nothing but support from my peers and mentors.

Next, I started working on an intra-company awareness initiative. Michael Danvers has been doing so much incredible work alongside the engineering and product teams, and we wanted more people throughout the company to be aware of our mission. I pitched the idea of a book club. I was immediately partnered with Monika Pierce and she was so incredibly passionate and receptive to include disability in our companies’ discussions of inclusion and diversity. I am so looking forward to this project coming to fruition and can’t wait to see this transformative community-wide dialogue continue.

Lastly, I worked towards hosting a virtual meetup around the topic of accessible technology. A meetup was something that Michael had been working towards and this summer was the perfect opportunity to test the new virtual landscape. When reaching out to WW designers and engineers I was met with so much positivity and support. The community mentality of WW employees truly shined, and it made the work so much easier. Putting on a meetup in the virtual world was certainly a learning curve but was one that exposed me to the inner workings of a large corporation and one that taught me countless lessons along the way.

At each part of my internship with WW, their impact manifesto rang true — WW is a company striving to democratize wellness and make it accessible to all. I am so incredibly thankful to have had the honor of participating in this journey and l can’t wait for what’s to come.

Kate Allan, Accessibility Educational Specialist Intern

WW: My Virtual Summer Internship Experience was originally published in WW Tech Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.

WW has a rich internship program, and this summer we hosted 34 interns in our New York City and San Francisco offices. In addition to the work they did with their teams, these students participated in Lunch and Learns with members of our leadership team and attended sessions to help them build their professional brand, such as resume review workshops and mock whiteboard training. But it wasn’t all work! Throughout the summer, WW’s interns also had the chance to get to know members of our team through offsite outings and socials. Read on for just some of the highlights of the internship program — in their own words.

Leah TenyEyck, Accessibility Specialist Intern, New York, NY

I had the honor of working on the proposal for the WW Accessible Innovations Lab. This was my first time creating a proposal, but my manager provided invaluable mentorship and guidance throughout the process. My goal was to create a lab that can produce a tangible, positive change at WW and create a system that makes it easy to integrate accessibility into the production flow.

The Accessibility Team is working to create a space where teams from Tech and Product can test their designs and theories using accessibility devices. Our goal is to create a space that encourages collaboration and exploration by providing the resources that teams need to integrate accessible protocols into their work.

We approached this problem in the same way that PMs do. After speaking to numerous experts within WW, I was able to use the RICE prioritization model, create a viable product roadmap, classify our project’s MVPs, KPIs, and use them to plan for future iterations. It’s been exciting to have a chance to work on both accessibility protocols as well as project management! This project has shown me a new way to approach problems and create solutions.

Throughout my research for this Lab, I had the chance to meet and discuss accessibility with specialists from companies like IBM, Verizon, AT&T, and Facebook. It’s been truly surreal to have the opportunity to work directly with some of my heroes in the industry.

Srineetha Maddineni, Product Management Intern, New York, NY

I’m a Product Manager Intern on the Studio Tech team. Our goal is to revamp the current app by building a user-friendly iOS app that reduces time spent in transactional activities, improves omnichannel customer experience, and offers more personalized in-person and online communication with members. Over the course of my internship, I focused on building three features that can enable these goals. I had an amazing learning experience with a steep learning curve where I learned about everything from requirements gathering and UI/UX design to engineering and QA in an agile environment. My manager was extremely supportive and gave me the freedom to lead the process, which I’m grateful for as I’m a strong believer in learning by doing. One highlight of my internship was deciphering the B2B side of the studio business (which is extremely convoluted) by talking to stakeholders from across the world.

Ashten Bartz, UX Researcher Intern, San Francisco, CA

This summer, I worked with the Experience Research team to conduct qualitative research by interviewing WW members. It’s been really rewarding to hear how WW has changed the lives of so many individuals. Learning more about members’ needs through these interviews helps the Product and Tech teams design tools for wellness that really work. One of the highlights of my summer was attending studio workshops in the Bay Area. Although I arrived with the intention of simply recruiting members for research opportunities, I left feeling inspired by the community, the coaches, and the experience of seeing WW’s mission in action.

Songren Zhao, AI Full Stack Software Engineering Intern, New York, NY

I worked on the backend team of Studio Tech as a software engineer intern. Studio Tech is responsible for the systems that customers interact with and employees use to create meaningful, personalized experiences in our WW Studios across the globe. One thing that separates WW from other companies is that we have a chance to contribute to the codebase of the company. Interns are not treated differently. Instead,we work closely with full-time employees. Also, WW focuses on cutting-edge technology. I enjoyed our weekly developer discussions, where we discussed the tech stacks and coding practices that we follow.

Swetha Geetheswaran, Android Engineer, New York, NY

My internship at WW as an Android Engineer was a great learning experience and helped me grow both professionally and personally. Being curious and staying positive helped me learn new concepts everyday. I even had the opportunity to learn a new language called Kotlin during my very first task! My team was very supportive, which allowed me to make important contributions. Through Lunch and Learn sessions, I had the opportunity to interact with our CEO, CTO, and CPO, and learned about WW’s impact on a much broader level. The Mock Interview session and the Resume review workshop helped me further improve my interviewing skills. As a young female engineer in tech, I’ve found WW to be an inspiring work environment to be in. I’m very thankful for all the opportunities I had during my internship experience and I’m confident that the skills and experience I’ve gained will help me find success after graduation.

Jeffrey Hao-Chan Huang, Software Engineering Intern, New York, NY

I worked as a software engineering intern on the eCommerce team over the course of this summer. In addition to gaining first-hand experience on how to implement and extend services supporting the eCommerce platform, I’ve also gained a unique understanding of how technology fits into the context of promoting wellness and health. The patient guidance of my team members as well as their willingness to let me take on greater responsibilities enabled me to continue learning and contributing throughout the internship. One highlight of this summer was the Lunch and Learn sessions in which the interns are given the opportunity to have in-depth discussions with executives on their experience and unique perspectives.

Interested in joining the WW team? Check out the careers page to view technology job listings as well as open positions on other teams.

WW: My Summer Internship Experience was originally published in WW Tech Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.