About Us

We're on a mission to transform spoken communication for individuals, teams and organizations. Meetings are an information rich channel for productivity, but much is lost due to lack of structure and information flow. 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. We are growing rapidly and are excited for new teammates to join. We hire people who are the best at what they do. We are 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 gatherings 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'll lead the full development cycle of AI solutions for meeting notes, question answering, agent conversations, and task completion. Your responsibilities will include prompt engineering, agentic workflows using LLM APIs, custom model training, fine-tuning, and optimizing deployment for cost, latency, and quality. Key projects include:

Prompt engineering using state-of-the-art techniques to improve the core meeting assistant scenarios
Building and shipping machine learning models to improve transcript quality, reduce API token usage, eliminate LLM output defects, and extract semi-structured data
Training and deploying custom language models (LoRA, RLHF, instruction-tuning, etc.), fine-tuning models for diverse business needs
Creating new NLP & LLM-driven product experiences that improve with user feedback, collaborating with product and design teams
Improving our LLM-powered search and question answering using retrieval augmented generation (RAG), everything from defining and improving quality metrics to optimizing our infrastructure
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 reliable, high quality production code for deployment. Requirements for the job include:

Production-level AI/ML Experience: Demonstrated proficiency in AI/ML with a track record of at least 3-5 years experience building machine learning systems. We require skills for shipping and maintaining ML in production settings
Experience delivering products and services using GenAI: 1+ year experience building NLP systems using LLMs, including proficiency with data curation/cleaning/labeling, fine-tuning/training models, and evaluating generative text output. Experience with RAG systems or conversational agents is a plus
Software Engineering Competency: A solid engineering background with a robust foundation in software engineering principles. Must have comfort and excellence in writing code for and supporting production engineering systems.
A Solid Educational Foundation in AI/ML: Bachelor's degree in Computer Science, Engineering, AI, Mathematics, or related field; Master's degree or PhD a plus
Proficient in Python: our AI stack uses Python and interfaces with Ruby on Rails and Typescript (bonus if you know these, but not required.)

What we'll expect of you

A collaborative mindset, focused on lifting others and improving daily
A drive to tackle tough problems and do hard things
High agency and initiative in bringing and building ideas
Willingness to learn and improve existing approaches
Emphasis on shared ideas and collective responsibility
A growth mindset when facing challenges, aiming for team improvement

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)

Supernormal jobs

Machine Learning Engineer

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