About the role

Join the mighty team at Supernormal bringing the future of work closer to the present. We're a rapidly growing platform solving a real need for thousands of people every day – automating meeting notes and tasks. As an engineer at Supernormal, you’ll play a major role in developing our product experiences and a workplace that people love. Together we’ll help people save time and meet more efficiently so our customers can focus on what matters – like sending satellites to space or running their local government.

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.

Supernormal is a well-funded growth stage startup backed by EQT Ventures, Balderton Capital, and byFounders VC. We’re growing rapidly in a competitive market with an AI-powered product used by thousands every day. If you want to operate with high autonomy in an environment where you’ll get to flex your skills to build great products with great people, Supernormal is your place.

What you’ll work on

The AI team at Supernormal owns everything about how meeting notes, question answering, and task completion are generated. This includes LLM API calls, custom model training and deployment, speech recognition, quality evaluation and fixes, retrieval augmented generation, and much more. We optimize for cost, latency, and quality. Some of the projects 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 to improve transcript quality, reduce tokens sent to APIs, remove defects in LLM output, and extract 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 how we use 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

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 2 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

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 (Seattle) 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

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Machine Learning Engineer