This role exists to translate cutting-edge AI research and experimentation into scalable product capabilities — ensuring that the tools, workflows, and systems our Data Lab uses are aligned with how modern AI models are actually trained, evaluated, and deployed.

You will work closely with research scientists, applied ML engineers, and product teams to:

• accelerate experimentation
• improve reproducibility and iteration velocity
• and decide which research outputs should become real, durable product features

This is a role for someone who understands frontier AI deeply , but chooses to apply that understanding through product judgment rather than research authorship.

What You’ll Do Productize Frontier AI Workflows

• Partner closely with Data Lab scientists to understand how models are being trained, evaluated, and iterated today
• Translate experimental workflows (data curation, labeling, evaluation, fine-tuning, feedback loops) into scalable product and platform capabilities
• Identify patterns across experiments that are worth standardizing versus those that should remain bespoke

Build Tools That Reflect How AI Is Actually Built

• Lead product discovery and execution for internal tools that support modern AI development:

• dataset versioning

• evaluation pipelines

• annotation and human-in-the-loop workflows

• experiment tracking and reproducibility

• Ensure tooling reflects real-world frontier practices, not academic abstractions

Be a Bridge Between Research and Product

• Serve as the primary product interface for the Data Lab
• Translate research intuition into product requirements engineers can build against
• Help researchers reason about tradeoffs between novelty, robustness, and scalability
• Collaborate with Platform and Vertical PMs to ensure new capabilities integrate cleanly into customer-facing products

Exercise Strong Product Judgment

• Decide when an experimental capability is ready to move from “research mode” to “product mode”
• Apply an 80/20 mindset without undermining scientific rigor
• Sunset or deprioritize tools and ideas that do not meaningfully advance AI development velocity or data quality

Measure Impact, Not Activity

• Define success metrics tied to:

• experiment cycle time

• researcher productivity

• adoption of internal tools

• downstream impact on customer data products

• Use qualitative and quantitative feedback to continuously iterate

Who You Are Deeply Fluent in Modern AI

• You have hands-on or adjacent experience with how frontier AI models are built today — including large-scale training, fine-tuning, evaluation, and data iteration

• You understand concepts like:

• training data quality vs quantity tradeoffs

• evaluation benchmarks vs real-world performance

• human feedback loops

• multimodal data challenges

• You can have credible conversations with PhD-level researchers and senior ML engineers

A Product Thinker, Not a Researcher

• You don’t need to publish papers — but you need to understand them
• You excel at turning complex technical systems into clear product decisions
• You enjoy asking: “What problem does this actually solve, and at what scale?”

Experienced Product Manager

• 5+ years of product management experience, ideally in:

• AI/ML platforms

• developer tools

• data infrastructure

• or internal research tooling

• Strong experience working with highly technical stakeholders

• Proven ability to lead ambiguous, zero-to-one initiatives

Collaborative and High-Agency

• Excellent communicator across research, engineering, and product
• Comfortable influencing without authority
• Bias toward shipping, learning, and iterating

Nice to Have

• Prior experience working with or adjacent to frontier model builders
• Experience with multimodal AI systems (text, audio, video, healthcare data)
• Background in ML engineering, data science, or applied research before PM

Why Protege

• Work directly on the infrastructure powering frontier AI development
• Partner with world-class researchers and product leaders
• Shape how experimental AI capabilities become scalable, real-world products
• Competitive compensation, equity, and benefits