About Us
We're on a mission to transform spoken communication for individuals, teams and organizations of any size. Meetings may be our most information rich channel for work, but suffer from a lack of structure and documentation. At Supernormal, we're solving this problem with focus, design and craft.
We've been working on this since 2019 and have customers like Snap, Salesforce, Replay, Gitcoin, Pinterest and thousands more on this journey with us. Today, we are growing rapidly and are excited for new teammates to join who are the best at what they do. We're passionately building a team that is as diverse and creative as the millions of people we serve worldwide.
Supernormal is a remote first company and does not require co-location. We have annual team retreats and gather several times a quarter.
About The Role
Machine learning engineers at Supernormal build the AI that superpowers the core product experience for people's meetings including transcription, note generation, and task automation. The AI team builds reliable and secure services that use the most advanced AI models in the market to generate millions of high-quality meeting notes to a rapidly growing customer base. Our work revolves heavily around software engineering, too - we are looking for people with a drive to roll up their sleeves and get new models and features out to users as quickly as possible.
What You'll Work On
As an ML engineer on Supernormal's AI team, you will be responsible for the end-to-end development of our AI solutions for meeting notes, question answering, and task completion. Your work will encompass LLM API calls, custom model training and deployment, speech recognition, quality evaluation and fixes, retrieval augmented generation, and much more. You'll play a key role in optimizing for cost, latency, and quality. Some of the projects you'll work on include:
- Prompt engineering using state-of-the-art techniques to improve the core meeting assistant scenarios
- Building and shipping custom machine learning models to augment the AI stack, including improving transcript quality, reducing tokens sent to APIs, removing defects in LLM output, and extracting semi-structured data
- Training and deploying custom large language models from open source using state-of-the-art techniques (LoRA, RLHF, instruction-tuning, etc) and fine tuning foundation models for a variety of business purposes
- Developing new product experiences using NLP & LLMs that get better based on user feedback & iteration while collaborating with product engineers & design team
- Defining and improving business & product metrics to optimize the quality and cost of AI usage
- Improving LLM-powered search and question answering (using RAG) over sets of meetings
- Advocating for, and building, new and better ways of doing things. You'll leave everything you touch just a bit better than you found it
Requirements
What you will bring
We are a fast-moving startup building zero-to-one products on top of large language models. The ideal candidate has a strong machine learning background and a hacker mindset, someone who can both spin up Jupyter Notebooks to train models and also excel at writing solid, fast production code for deployment.
- AI/ML Experience: Demonstrated proficiency in AI/ML with a track record of at least 3-5 years experience building machine learning systems, up to speed on the latest in NLP & LLMs, and proficient in data curation, modeling, and training models. We require skills for shipping and maintaining ML in production settings
- A Solid Educational Foundation: Bachelor's degree in Computer Science, Engineering, AI, Mathematics, or related field; Master's degree or PhD a plus
- Versatile Software Engineering Skills: A solid engineering background with a robust foundation in software engineering principles. You have written code for and supported production engineering systems.
- Proficient in Python and SQL: our AI stack uses Python & PyTorch and interfaces with Ruby on Rails (bonus if you know it, but not required) and we write a lot of SQL queries on top of Snowflake to pull data
What we'll expect of you
- A collaborative and open outlook — we're all about lifting each other up and getting better every day
- A willingness to get deep into a problem even when it seems impossible. You'll always have support from the team
- Confidence operating with high agency. We'll work together to decide what's important, but we'd love for you to bring (and build!) your own ideas
- You'll come in willing to learn why things are the way they are, then suggest a better way
- You'll understand that there's no difference between "my idea" and "their idea." It's our ideas and we're all responsible for it
- You'll approach speed bumps and reviews through a "how can the team level up?" lens — let's all get better and learn, together
What you can expect from us
- We're a fully distributed team spread between Pacific Time (California) and Central European Time (Stockholm) with lots of places in between. We'll see you most days in Slack, Google Meet, GitHub issues, and Notion. Sometimes in person in a place with a warm breeze
- We're a friendly bunch and are happy to pair, talk through, or otherwise assist any time
- Honest and timely feedback. We're all better when we can have candid conversations about what is and isn't working
- A willingness to listen to your ideas: how can the codebase, our product, or team be better?
- A respect for your time outside of work. We all work hard here, but we never forget to rest and have fun
Benefits
💰 Competitive salary, 401K
📈 Stock options
🏥 Full healthcare coverage (Medical, Dental, and Vision)
🚀 Totally remote. Not hybrid. Remote. No return-to-office here
🏠 WFH budget to make sure you have everything you need to do your best work
✈️ Annual team-wide offsite to someplace cool
🎓 Education credit (up to $500 per year)
🧳 Unlimited PTO (minimum 4 weeks)
