Machine Learning Engineer

About Us

We're Supernormal, a dynamic company on a mission to transform spoken communication for individuals, teams, and organizations of all sizes. Meetings are our most information-rich channel for work but often lack structure and documentation. At Supernormal, we address this challenge with focus, design, and craftsmanship. Since 2019, industry leaders like Snap, Salesforce, Replay, Gitcoin, Pinterest, and thousands more have joined us in this journey. We are rapidly expanding and excited to bring on new teammates who excel in their fields. We are committed to building a diverse and creative team that mirrors the millions of people we serve globally.

Supernormal is a remote-first company with no co-location requirements. We hold annual team retreats and gather several times each quarter.

About The Role

As a Machine Learning Engineer at Supernormal, you'll develop AI solutions that power our core product features like meeting transcription, note generation, and task automation. You will build reliable and secure services using advanced AI models to deliver high-quality meeting notes to a growing customer base. This role will require hands-on software engineering, ideal for those driven to implement new models and features swiftly.

What You'll Work On

As a Machine Learning Engineer on our AI team, you will oversee the full-cycle development of AI solutions for meeting notes, question answering, and task completion. Responsibilities include:

• Prompt engineering using state-of-the-art techniques to enhance core meeting assistant features.
• Building and deploying custom machine learning models to improve transcript quality, reduce tokens sent to APIs, and extract semi-structured data.
• Training and deploying custom large language models from open source with techniques like LoRA, RLHF, and instruction-tuning for various business needs.
• Developing new product experiences with NLP & LLMs that improve based on user feedback and collaboration with product engineers and design teams.
• Defining and optimizing business and product metrics to balance the quality and cost of AI usage.
• Enhancing LLM-powered search and question-answering capabilities using retrieval-augmented generation (RAG) over multiple meetings.
• Advocating and implementing improved processes, leaving everything you touch better than you found it.

Requirements

What You Will Bring

We are a fast-moving startup developing cutting-edge products on large language models. The ideal candidate will have a solid machine learning background and a hacker mindset to spin up Jupyter Notebooks for model training and write efficient production code for deployment.

• AI/ML Experience: At least 3-5 years of hands-on experience in building machine learning systems, deep knowledge in NLP & LLMs, and proficiency in data curation, modeling, and model training. Experience in deploying and maintaining ML models in production is required.
• Solid Educational Foundation: A Bachelor's degree in Computer Science, Engineering, AI, Mathematics, or a related field. A Master's degree or PhD is a plus.
• Versatile Software Engineering Skills: Strong foundation in software engineering principles, with experience in writing and supporting production code.
• Proficient in Python and SQL: Experience with Python & PyTorch, as well as SQL queries on Snowflake. Familiarity with Ruby on Rails is a bonus.

What We’ll Expect Of You

• A collaborative and open outlook — we lift each other up and improve every day.
• A willingness to delve deep into problems and tackle challenges head-on with team support.
• Confidence in making decisions with high autonomy while also contributing original ideas.
• A mindset to learn existing systems and suggest improvements proactively.
• An understanding of collaborative idea ownership — "our ideas" over individual contributions.
• A focus on team improvement through constructive feedback and mutual learning.

What You Can Expect From Us

• A fully distributed team located between Pacific Time (California) and Central European Time (Stockholm), frequently interacting via Slack, Google Meet, GitHub issues, and Notion.
• A friendly environment with team members ready to pair, discuss, or assist at any time.
• Honest and timely feedback to foster personal and team growth.
• Open ears for