Machine Learning Engineer
3 weeks ago
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 quarterly.
About the roleMachine 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 onAs 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).
- 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 bringWe 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.
- 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
- 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.
Benefits
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