About usWe’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 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’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.