Manifold Bio
Product Manager, AI Platform & Partnerships
$165K–$275K
Boston or San Francisco +2 · Manager · Full-time · Posted May 3, 2026
- Location
- Boston or San Francisco
Boston, Massachusetts, United States
San Francisco, California, United States - Experience
- 2–5 years; experience may be in product management, technical program management, alliance management, or BD, with at least part in AI/ML or computational product environments.
- Education
- Bachelor’s degree in a technical field (CS, engineering, biology, or related); advanced degree a plus.
About the job
Own the internal AI platform roadmap and manage external AI partnerships, translating AI and biology capabilities into products and platform assets.
What you'll do
- Define the AI platform vision, roadmap, and success metrics, and translate R&D ideas into requirements and operating plans.
- Prioritize, develop, and close AI partnerships; coordinate cross-functional execution and manage external deliverables.
- Produce executive-ready memos, reports, decks, and demos to align stakeholders.
What they ask for
- 2–5 years of product management, technical program management, alliance management, or BD experience, with some in AI/ML or computational product environments.
- Bachelor’s degree in a technical field (CS, engineering, biology, or related).
- Working knowledge of modern AI/ML concepts and their applications to drug discovery; familiarity with biology, protein engineering, drug discovery, or another applied scientific domain.
- Strong written communication, cross-functional execution, strategic relationship management, and agentic coding skills.
Nice to have
- Experience building or shipping products at a frontier AI lab, AI infrastructure company, or AI-first startup.
- Experience structuring partnership terms, scaling partnership functions, or public communication through writing or talks.
Pay
$165K-275K (base salary)
Tools
AI/ML, foundation models, training and evaluation, agentic systems, scaling laws
AI focus
Lead product strategy for AI capabilities including protein design, foundation models, reasoning models, agents, and infrastructure; apply AI/ML to scientific problems and drug discovery.
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