Product Manager, Invest
•OnePay· 15h
📦 Product⏰ Full-time🎯 Lead / Principal💰 $160k-180k🇺🇸 USA
15h ago
Protege·about 1 month ago
Company Overview:
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.
Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.
We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.
We’re hiring a Product Manager, Data Lab to sit at the center of Protege’s research and innovation engine.
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.
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
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
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
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
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
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
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?”
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
Excellent communicator across research, engineering, and product
Comfortable influencing without authority
Bias toward shipping, learning, and iterating
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
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