Strategic Product Manager, Scale AI

• PhD in Computer Science, Machine Learning, or AI (or equivalent research experience) from a top university
• 5–10+ years in product/research/ENG roles working with LLMs or multimodal systems; hands-on with post-training and evaluations
• Strong technical fluency: you can read papers, interrogate metrics, write or review complex Python/SQL for analysis, and reason about model-data trade-offs
• Executive presence with world-class researchers and enterprise leaders; excellent writing and storytelling
• Comfort in the field: you like joining sales calls, whiteboarding solutions, and turning ambiguous asks into scoped plans
• Bias to action: you ship, learn, and iterate

Desirable

• We’re hiring a Strategic Product Manager to sit at the intersection of frontier AI research, product, and go-to-market
• You’ll partner closely with account executives in high-stakes meetings, scope and pitch solutions to top AI labs, and translate research needs (post-training, evals, alignment) into clear product roadmaps and measurable outcomes
• You’ll own end-to-end delivery—from discovery and PRD to pilot, launch, and iteration—with deep cross-functional leadership across research, engineering, ops, and finance
• Ride shotgun with AEs: join customer pitches, shape solutions live, and convert needs into scoped SOWs and product plans
• Translate research → product: work with client side researchers on post-training (SFT/RLHF/RM/DPO/GRPO), evals, safety/alignment and build the primitives, data, and tooling they need
• Own full lifecycle: drive discovery, write crisp PRDs, prioritize trade-offs, run experiments, ship v1s, and scale successful pilots into repeatable offerings
• Lead complex customer programs: independently run high-stakes sessions with senior stakeholders; set success metrics; communicate risk and path to green
• Partner across Scale: collaborate with research (agents, browser/SWE agents), platform, ops, security, and finance to deliver dependable results for customers
• Build evaluation rigor: stand up eval suites (RLVR/benchmarks), close the loop with data quality, and publish internal learnings that raise the bar across accounts
• Customer-obsessed: start from real research needs; prototype quickly; validate with data
• Cross-functional by default: align research, engineering, ops, and GTM on a single plan; communicate clearly up and down
• Field-forward: expect regular customer time alongside our AEs and research leads; light travel as needed
• Success looks like:
• Clear wins with top labs: pilots that convert to scaled programs with strong eval signals
• Reusable alignment & eval building blocks that shorten time-to-value across accounts
• Crisp internal docs (PRDs, experiment readouts, exec updates) that drive decisions quickly