Technical Program Manager - Embedded Evaluations and Investigations
Summary
As Technical Program Manager at Transluce, a nonprofit AI-evaluation research lab, you scope, organize, and run end-to-end evaluation engagements for frontier AI labs, governments, and civil society partners — coordinating technical staff, owning partner relationships, and communicating results. The role is in-person in San Francisco with regular travel.
- Define the scope of each engagement, set key milestones, and negotiate terms, with support from our operations team.
- Organize the technical team's work, manage dependencies, and keep everyone aligned through frequent touchpoints.
- Act as a sounding board on key strategic and methodological questions, reflecting the needs of the engagement and other considerations, such as relevant regulatory guidance.
- Own the day-to-day partner relationship, building trust and resolving issues while protecting Transluce's core interests and the technical team's time.
- Engage directly with evaluation results and technical analysis to understand the work, identify issues, and help drive the engagement forward.
- Present deliverables to partners and summarize results for broader audiences, including blog posts, papers, and government and media briefings.
- Understanding of AI evaluations and the ability to engage with technical teams on their design and results.
- Track record of managing cross-functional teams, for example in a tech company, consulting firm, or fast-paced startup.
- Experience engaging external partners such as frontier AI companies, government actors, or civil society organizations.
- Ability to work through ambiguity and deliver against complex, evolving goals on short timelines, building workflows where none exist.
- Ability to translate technical research for a range of audiences.
- Exceptional communication and relationship skills, low ego, openness to giving and receiving feedback.
- Experience managing evaluations at scale or in a production context.
- Proficiency in Python and data analysis tools (e.g., pandas, SQL), and familiarity with common ML and evaluation frameworks (e.g., PyTorch, Hugging Face, Inspect).
- Experience building and applying software tools, for example in a forward-deployed engineering role.
- Knowledge of frontier AI risk management practices or relevant regulatory guidance.
