Engineering Manager, Model Library

Responsibilities

• Lead and grow a team of engineers, including hiring, mentorship, and career development.
• Own the Model Library product area end-to-end — from discovery and evaluation experiences to the APIs and tooling developers use to integrate models.
• Partner with product, ML, and cross-functional stakeholders to define scope, prioritize work, and communicate progress.
• Maintain a high technical bar through design doc reviews, code reviews, and hands-on architectural guidance.
• Drive product quality and reliability: define success metrics, establish feedback loops, and ensure a consistent, high-quality developer experience.
• Foster a culture of strong written communication, clear design thinking, and effective collaboration.

Requirements

• Experience managing engineers building developer-facing products: APIs, SDKs, or developer tooling.
• Product intuition and technical judgment — able to balance speed, quality, and developer experience tradeoffs.
• Technically hands-on enough to review designs, debug issues, and earn the trust of a strong IC team.
• Strong written communication and cross-functional collaboration skills.
• Interest in AI/ML and the evolving model landscape; willingness to develop deep domain knowledge (ML expertise not required).
• Experience building or supporting self-serve workflows.

Nice to Have

• Background as an IC in developer tooling, APIs, or ML infrastructure before moving into management.
• Experience with model evaluation, benchmarking, or comparison frameworks.
• Familiarity with inference platforms, LLM runtimes, or the model ecosystem (open-source and frontier).

Benefits

• Competitive compensation, including meaningful equity.
• 100% coverage of medical, dental, and vision insurance for employee and dependents.
• Flexible PTO policy including company wide Winter Break (offices closed from Christmas Eve to New Year's Day).
• Paid parental leave.
• Fertility and family-building stipend through Carrot.
• Company-facilitated 401(k).
• Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.