MP
$405,000 – $485,000
Listed on Menlo Ventures Portfolio’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
Lead Anthropic's biological safety research team, managing scientists and engineers who evaluate how AI models handle biological knowledge and build safeguards to prevent misuse while keeping legitimate research unimpeded. This hands-on role combines people leadership with deep technical work in ML safety, biosecurity, and life sciences.
Our summary, not Menlo Ventures Portfolio’s wording. The full posting is on their site.
Skills this role names
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What they ask for
Required
- Experience managing a technical team with hiring, coaching, and performance management
- Track record setting technical direction and prioritizing under uncertainty
- Proficiency in Python with scientific programming and data analysis background
- Understanding of ML fundamentals sufficient to review evaluation and classifier design
- Knowledge of modern biology including high-throughput assays, functional characterization, gene synthesis, genome editing, strain construction, and protein engineering
- Experience designing quantitative experiments and drawing conclusions from noisy results
- Strong analytical and writing skills with ability to explain technical concepts to non-technical stakeholders
- Familiarity with dual-use research concerns and biosecurity frameworks such as Select Agent regulations or Biological Weapons Convention
- Comfort with ambiguity and shifting priorities
- Commitment to preventing misuse without obstructing beneficial life sciences research
Nice to have
- 3+ years of people management experience, ideally leading research scientists or ML engineers
- Experience building a team or function from small headcount
- At least 8 years of hands-on life sciences experience with deep expertise in molecular biology, drug discovery, or computational biology
- Experience working with large language models including prompting, fine-tuning, or evaluation
- Experience training or deploying classifiers in production with understanding of precision and recall for rare, high-consequence categories
- Experience developing ML methods for biological systems or data
- Familiarity with adversarial robustness, red-teaming, or safety evaluation of ML systems
- Experience leading complex technical projects across multiple stakeholder groups