Technical Program Manager, Research
Listed on 2026-07-27
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Research/Development
AI Business & Operations, Data Scientist, Research Scientist, AI Evaluation
About Us
FAR.AI is a non-profit AI research institute working to ensure advanced AI is safe and beneficial for everyone. Our mission is to facilitate breakthrough AI safety research, advance global understanding of AI risks and solutions, and foster a coordinated global response.
About Us
FAR.AI is a non-profit AI research institute working to ensure advanced AI is safe and beneficial for everyone. Our mission is to facilitate breakthrough AI safety research, advance global understanding of AI risks and solutions, and foster a coordinated global response.
Since our founding in July 2022, we ve grown to 45+ staff, published 40+ academic papers including an ICML 2026 Outstanding Paper Honorable Mention, and convened leading AI safety events. Our work is recognized globally, with publications at premier venues such as NeurIPS, ICML, and ICLR, and features in the Financial Times, Nature News and MIT Technology Review. We conduct pre-deployment testing on behalf of frontier developers such as OpenAI and independent evaluations for governments including the EU AI Office.
We help steer and grow the AI safety field through developing research roadmaps with renowned researchers such as Yoshua Bengio; running FAR.Labs, an AI safety-focused co-working space in Berkeley housing 40 members; and supporting the community through targeted grants to technical researchers.
About Research
FAR.AI conducts research to address fundamental artificial intelligence (AI) safety challenges. We rapidly explore a diverse portfolio of technical research agendas, de-risking and scaling up only the most promising solutions. We share our research outputs through peer-reviewed publications, via partnerships with governmental AI safety institutes, and through red-teaming engagements for leading AI companies.
FAR.Research is dedicated to delivering the novel technical breakthroughs needed to mitigate the potential risks posed by frontier AI. As a non-profit research institute, we leverage our unique flexibility to focus on critical research directions that may be too large or resource-intensive for academia and often overlooked by the commercial sector due to their lack of immediate profitability.
About
The Role
We are looking for Technical Program Managers to define programs for both our foundational research and research infrastructure teams. Our Research organization is scaling up by expanding our portfolio of research agendas, including model deception, applied interpretability, evaluation awareness, pre-training filtering, and open-weight safety. As our Research portfolio expands, our Research Infrastructure needs will also scale up to meet these needs.
You ll find the highest-leverage problems and own them end to end, building the processes, systems, and tooling that help our researchers and engineers move faster.
What You ll Do
- Take complex technical programs from idea to delivery: scope the plan and milestones, then keep complex, multi-team work moving efficiently, transparently, and to a high technical bar.
- Prioritize across competing demands in partnership with research teams so the teams’ effort lands on the highest-impact work.
- Build and maintain compute and resource roadmaps by surfacing bottlenecks and trade-offs, finding ways to accelerate progress and build relationships with potential external partners.
- Partner with researchers to package their work effectively to get our research into the hands of people who can act on it by transferring findings, methods, and tools to frontier labs and other stakeholders.
- Connect research and engineering with our internal partners in Operations, Events and Communications giving each the timelines, results, and context they need to plan, resource, and communicate our work.
- Are a fast learner and comfortable with ramping up quickly on unfamiliar technical domains to hold your own in discussions with researchers and engineers.
- Can reason about technical trade-offs in depth (e.g. measuring quality of agent output balanced against credit spend, compute efficiency, and training and inference infrastructure) to facilitate clear decisions by stakeholders.
- Enjoy…
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