Machine Learning Engineer, Frontier Data Products

Highlights

• Build ML systems that score and validate complex expert work for frontier models.
• Design evaluation frameworks for ambiguous tasks with noisy, disputed labels.
• Own feedback loops turning human review into compounding model improvements.

What they offer

• Bi-annual performance bonus structure
• Generous equity grant vested over 4 years
• Up to $15k relocation bonus
• $10K housing bonus if within 0.5 miles of office
• $1.5K monthly meals stipend
• Free Equinox membership
• $200 monthly laundry reimbursement
• $200 monthly wellness reimbursement
• Health, Dental, Vision insurance

Ideal candidate

Applied ML builder who ships simple, inspectable systems and obsesses over real-world output over benchmarks. Comfortable with ambiguity and motivated to define how models and experts work together.

Requirements

• Track record of shipping ML systems that improved a real product, workflow, or business metric
• Strong instincts for model quality, evaluation design, error analysis, and production failure modes
• Comfort operating in ambiguous spaces where labels are imperfect and correctness evolves
• Sound judgment on when to use prompting, fine-tuning, heuristics, retrieval, or human review
• Solid engineering fundamentals across the full ML stack
• Familiarity with LLM applications, evaluation frameworks, or human-in-the-loop ML is a strong plus