Machine Learning Engineer - Focus on Modeling

About us

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 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 with a focus on modeling at Supernormal, you will specialize in developing and refining models that power our AI solutions for meeting notes, question answering, and task completion. Your expertise will be critical in LLM API calls, custom model training and deployment, speech recognition, quality evaluation and fixes, and retrieval augmented generation. You will primarily focus on optimizing model performance, 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).
• Specializing in model optimization to enhance performance and efficiency.
• 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.

What you will bring

We are a fast-moving startup building zero-to-one products on top of large language models. The ideal candidate is passionate about machine learning modeling, with a foundational understanding of algorithms and hands-on experience in developing and optimizing models. You exhibit proficiency in basic data analysis, feature engineering, and model evaluation, with a willingness to learn and grow in the field.

• AI/ML Experience: 2-3 years of demonstrated experience working on real-world machine learning projects. This includes proficiency in developing and optimizing machine learning models, along with skills in model evaluation, validation, and tuning. Familiarity with handling large datasets, data preprocessing, and feature engineering is essential. Experience in deploying models into production environments and monitoring their performance is highly desirable.
• A Solid Educational Foundation: Bachelor’s degree in Computer Science, Engineering, AI, Mathematics, or related field; Master’s degree or PhD in related disciplines is a plus.
• Software Engineering Skills: Proficiency in software development principles and best practices, such as version control, code review, and testing. Experience with collaborative development tools and platforms, such as GitHub, is essential. Familiarity with Agile methodologies and continuous integration/continuous deployment (CI/CD) pipelines is highly desirable.
• 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