Our mission is to make the best mental health tools radically accessible to everyone.
We’re a venture-backed startup building tools to help millions of people monitor, regulate and ultimately improve their moods. Our tools are powered by the latest research in NLP and ML, use scientifically proven techniques from Cognitive Behavior Therapy, and is based on 10 years of digital mental health research at Stanford University. Our Chairman is Andrew Ng and Dan Jurafsky is a member of our advisory board, both globally recognized leaders in AI/NLP.
We are poised to redefine how people access mental health care and are building the dream team that’s excited about getting us there. You will be working with modern tech such as Node.js, Docker, Tensorflow, AWS Lambda and more. Your ability to add new and critical features to Woebot while also refactoring parts of our current system will help provide the fuel that will help us get there.
WHY YOU SHOULD JOIN OUR DATA ENGINEERING TEAM:
As a Machine Learning Engineer, you will work closely with our Data Science, Product and Engineering teams to develop & productionize machine learning algorithms that are the core of Woebot’s intelligence. You will work with NLP to create a best-in-class conversational engine, manage data labeling teams to ensure high quality training data and pipelines, enable Woebot to deliver meaningful insights to users at scale, personalize Woebot’s content and relationship to each user, and execute experiments that allow Woebot to deliver the right method to the right person at the right time.
How You’ll Thrive
- In your first 2 weeks, you’ll learn about the Woebot content architecture and how ML and NLP are used to guide conversations with our users.
- In your first 3 weeks, you’ll list improvements that could be made to our existing set of classifiers.
Own Our Machine Learning Models, Systems & Processes
- During your first 45 days you will develop infrastructure for the full cycle of our machine learning efforts, this includes, model training, feature extraction, deploying produced models, data processing, and rigorously A/B testing.
- To accomplish this you'll collaborate with engineers to integrate algorithms efficiently with backend production services while helping define our data team's processes and tooling.
- You will also build machine learning models that enable Woebot to more naturally understand users’ natural language input, and generate appropriate responses. Initially you will prioritize the following:
- Improve Woebot’s Sentiment Analysis.
- Work with our Product team to define solutions and integrate them into a 1-year roadmap.
- Help scale our services using GPUs and modern distributed processing tools in the cloud (AWS).
- Dig into data with ad hoc analysis as necessary for technical, clinical, and user needs.
Help Woebot Have More Natural Flowing Conversations
- Within your first 60 days you will be responsible for gathering data and building data labeling systems so that Woebot continually learns from ongoing conversations to better recognize the sentiment and intent behind users’ natural language inputs.
- You will have unique datasets to work with, such as: 1M+ user conversations, support tickets, and other natural/unstructured data sources.
Turn Millions of Data Points Into Valuable Insights
- By day 90 you will create user profiles and build models that define how Woebot interacts with each user. We consider personalization to be key for developing a relationship over time, and delivering precision intervention - that is, methods that are tailored to each user.
- You will improve our machine learning models that enable Woebot to more naturally converse with and understand users. Combining intent classifiers, chit chat, and task-oriented models that help users achieve their goals of feeling happier while also feeling natural and conversational.
- After a few months you’re conducting deeper analysis to improve models that enable Woebot to derive insights about individual users, thus allowing it to give personalized feedback to users, such as “Did you realize that you’re happiest on Sundays, and least happy on Tuesdays?”
- To accomplish this you’ll work closely with our Product team to deliver these insights at the right time and in the right manner to help users gain new insights about themselves.
This Might Be Your Next Career Move IF:
- You care about helping make quality mental health care realistically accessible to millions of people nationwide.
- You passionately follow the latest trends in NLP and are excited by the challenge of applying the latest research to real-world problems.
- You want to get closer to the data and realize that advances in algorithms often come second to high-quality data.
- You love tackling big meaningful issues with data, even though they are often hard to measure.
- You've deployed natural language processing and/or deep learning models using spaCy and/or NLTK
- Experience deploying systems into production, at scale, using Docker, Kubernetes, or ECS
- Knowledge of one or more modern ML/NLP frameworks, such as PyTorch or Tensorflow
- Bachelors, Masters or PhD in Computer Science, Mathematics, Data Analysis or Machine Learning or related technical field with 1+ years of applied experience
- Ability to productize the latest research and establish a clear vision for how ML/NLP can be used in mental health
- Agile go-to-market product mindset: we do research and we innovate, but we also ship often
- Strong written and verbal communication skills
Our Core Values
- Empathic: Place a high value on user-experience. Motivated to help others be successful.
- Proactive & flexible: Hit the ground running. Even with ambiguity, you can get the job done.
- Self-awareness and growth-mindset: Wants to learn and grow in the role.
- High standards: Take pride in your work and apply high standards toward everything.
- Strong work ethic: Work hard to get the job done.
- Competitive Salary
- Health, Dental & Vision
- Employee Volunteer Program
Woebot is an equal opportunity employer and we deeply value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status