How to apply for Technical Program Manager, Research

About FAR AI

FAR AI is a nonprofit that bridges the gap between academic research and industry by incubating high-risk, high-impact AI safety projects. They focus on ensuring AI systems are trustworthy and beneficial, making them a unique destination for mission-driven technical program managers who want to directly contribute to AI alignment.

About the role

As a Technical Program Manager for Research, you'll own end-to-end delivery of research and infrastructure programs, working closely with cross-functional teams to scope milestones, manage resources, and accelerate progress. Your work directly enables researchers to focus on high-impact AI safety work without being bogged down by operational bottlenecks.

A typical day

Your day might start with a stand-up with research teams to check progress on compute utilization, then a deep-dive with engineering to resolve a resource bottleneck. Afternoon could involve updating program roadmaps, reviewing milestone deliverables, and a cross-team prioritization meeting to align on next quarter's goals.

Who FAR AI is looking for

• Proven experience managing technical programs in a research or engineering environment, ideally with exposure to AI/ML workflows.
• Strong ability to break down ambiguous research goals into clear milestones and coordinate multi-team execution.
• Comfortable making prioritization trade-offs in a fast-paced, resource-constrained environment.
• Familiarity with AI safety concepts (e.g., alignment, interpretability) is a strong plus, but not mandatory if you demonstrate technical curiosity.

Tips for this application

• Tailor your resume to highlight program management in research or R&D settings, not just software delivery.
• In your cover letter, explicitly connect your past experience to FAR AI's mission of accelerating AI safety research.
• Quantify your impact: e.g., 'Reduced compute bottlenecks by 30%' or 'Delivered 3 research projects on time despite shifting priorities.'
• Show you understand the unique challenges of managing research programs (e.g., unpredictable timelines, experimental nature).
• If you have any exposure to AI safety literature (e.g., papers from DeepMind, OpenAI, or MIRI), mention it briefly.

What to cover in your cover letter

['Your motivation for working in AI safety and why FAR AI specifically aligns with your values.', 'Specific examples of managing cross-functional research or engineering programs with clear milestones.', "How you've handled competing priorities and resource constraints in a technical environment.", "Your ability to learn new technical domains quickly (e.g., AI/ML concepts) even if your background isn't in AI."]

Research before applying

• Read FAR AI's published research and blog posts to understand their focus areas (e.g., mechanistic interpretability, scalable oversight).
• Familiarize yourself with the broader AI safety landscape and key organizations (e.g., MIRI, ARC, Anthropic).
• Check out their team page to understand the mix of researchers and engineers you'd be supporting.
• Review their job postings for other roles to get a sense of their organizational structure and culture.

Likely interview topics

Based on the job description, expect questions about:

• How would you scope a 6-month research program with uncertain outcomes? Walk us through your approach.
• Describe a time you had to reprioritize a project due to resource constraints. How did you communicate the change?
• What metrics would you use to track progress on a research infrastructure project like compute provisioning?
• How do you handle disagreements between research teams about project priorities?
• What do you know about FAR AI's current projects (e.g., interpretability, adversarial robustness)? How would you contribute?

Common mistakes to avoid

• Treating this like a standard tech PM role without acknowledging the research context and uncertainty.
• Not demonstrating genuine interest in AI safety or the company's mission (generic 'I love AI' is not enough).
• Overpromising on technical expertise; instead, show you can quickly learn and bridge communication between researchers and engineers.