About The Role
Join a small and mighty team at Supernormal working on pulling the future of work closer to the present. We're a rapidly growing platform solving a real need for thousands of people every day. As an early engineer at Supernormal, you’ll play a major role in building product experiences—and a workplace—people love. Together, we’ll help save real humans countless hours of manual work so they can spend their time 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 every month to a rapidly growing customer base. MLEs at Supernormal train and deploy custom ML models, engage deeply with LLMs through prompt engineering, fine-tuning, and neural search, and define & improve quality measures in service of our mission to make people more productive. You’ll also work closely with our product engineers and designers to ship user-facing product experiences powered by AI output.
Supernormal is a well-funded seed-stage startup backed by EQT Ventures, Balderton Capital, and byFounders VC. We’re growing rapidly in a shaky market which makes us one of the lucky ones. 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
- 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 manage transcript quality, compress text without losing semantic meaning, 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
- 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
- 1-2 years of industry experience as a machine learning engineer in NLP-related applications. Experience with large language models is preferred
- Bachelor’s degree in Computer Science, Engineering, Mathematics or similar field; Master’s degree or PhD a plus
- Ability to train and evaluate ML models (e.g. via Jupyter notebooks) to solve important business needs
- Experience deploying and operating ML models in production
- Ability to write robust production-level code in Python. Ruby experience is a plus. Experience working with data pipelines, reliable & secure production systems, data querying
- Deep knowledge of the underpinnings of machine learning: math, probability, statistics, and algorithms
- Plus if you have experience training large models on GPUs.
- Excellent communication skills
- Ability to work in a team
- Systems thinking and a deep desire to improve key metrics through engineering improvements
- Demonstrated experience working collaboratively with other engineers.
- A habit of making things better without waiting for being asked
- A desire and willingness to help even when things fall outside of your scope of responsibility
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
💰 Competitive salary
📈 Stock options
🏥 Full healthcare coverage (Medical, Dental, and Vision)
🚀 Flexible hybrid working model
🏠 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)