About the RoleYou'll own the quality bar for Notion AI products. You'll work with product and engineering teams to build systems to define what "good" looks like, measure our progress, and drive changes to deliver reliable and high-quality AI experiences. Your work directly shapes how Notion's AI products behave for millions of users.
This isn't a traditional software engineering role. It's an art & science role. You won't spend your days writing code. Instead, you'll focus on understanding and shaping how our AI products behave through context engineering, designing evaluation systems, and analyzing data. This team sits in our AI engineering team, working directly with engineering, product, design, and data.
This role is a unique blend of ops, strategy, and product thinking. Day to day, you'll live in production data, ship prompt fixes, run evals and, in effect, shape our quality strategy. As part of that you'll shape Notion's model strategy and work directly with frontier AI labs (OpenAI, Anthropic, Google) to evaluate and launch new models.
We're looking for
problem-seeking generalists interested in 0 1 1: curious people with high agency who thrive in ambiguous, fast-moving product areas. We're building a product, but also building a new function. You'll have real ownership from day one and help write the playbook as we scale.
What You'll Achieve- Context engineering - Design, test, and iterate on system prompts, tool prompts, and context strategies that shape how Notion's AI products behave. Understand the nuances of how models respond to different context structures and use that knowledge to drive quality improvements directly.
- Understand & debug - Live in production data: transcripts, logs, user feedback. Reproduce issues, identify root causes, and translate symptoms into actionable problem statements. Find signal in noisy data.
- Build evals & Measurement - Design eval strategies, build datasets, run evaluations. Track quality over time. Identify issues before users do. Own the loop: define quality goals, create evals, test and improve
- Evaluate and launch new models with leading research labs - Evaluate and launch models from OpenAI, Anthropic, Google, and others. Benchmark across dimensions: quality, latency, cost, edge cases. Help shape Notion's model strategy based on real data.
- Drive quality priorities - Work embedded with eng and product teams to surface the most important issues. Own the quality narrative: severity, frequency, what to fix and why. Be the voice of quality in the room.
- Build tooling & systems - Help manage AI observability and eval platforms (e.g., Braintrust). Build the playbooks and tools that enable all teams at Notion to build AI products.
Skills You'll Need to Bring- Driver mentality - You treat problems as yours. If something's broken, it's your job to fix it, even if you didn't cause it. You have a bias to action.
- Curiosity -You're excited about exploring the "jagged frontier" of LLM capabilities and how AI products meet reality
- Analytical instinct - Your first move is to look at data. You can find signal in noise.
- Comfortable working with data - You can self-serve insights from large datasets, whether through SQL, coding agents, or other tools.
- Clear communication - You can explain complex issues simply.
- Experience with LLMs, prompting, or AI products
Nice to Haves- Backgrounds in engineering, product, data science, research, consulting
- You've built something on your own to solve a problem - side project, startup, tool, whatever
Notion is committed to providing highly competitive cash compensation, equity, and benefits. The compensation offered for this role will be based on multiple factors such as location, the role's scope and complexity, and the candidate's experience and expertise, and may vary from the range provided below. For roles based in San Francisco or New York City, the estimated base salary range for this role is $98,000 - $140,000 per year.
A Note on AIYou don't need deep AI expertise for every role, but we do expect every Notino to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement - when that's the case, we'll say so explicitly in the qualifications. People who thrive here don't treat AI as a novelty. They use it to think better, and make their work easier for others to build on.
