About the role:
As a Technical Program Manager for Research, you'll drive initiatives focused on post-training research activities. You'll work closely with research teams and engineering to coordinate large-scale training workflows and improve infrastructure for reinforcement learning and other post-training techniques. This role is critical in orchestrating complex training runs that enhance model capabilities and driving technical improvements to our research infrastructure.
Responsibilities:
- Partner with researchers and engineers to understand and execute training plans
- Lead reinforcement learning infrastructure initiatives, coordinating between technical teams to improve efficiency, reliability, and scalability across all post-training workflows
- Build and maintain relationships with research teams to deeply understand their technical requirements, dependencies, and constraints
- Lead meetings and program reviews for post-training workstreams, ensuring alignment and progress across technical teams
- Facilitate coordination between research teams working on various aspects of model training
- Partner with engineers to implement and drive adoption of research tooling
- Maintain comprehensive documentation of research outcomes and collaborate with product managers to ensure research advancements are effectively communicated across the organization
- Provide detailed status reports, identifying technical risks, dependencies, and areas requiring additional support
You may be a good fit if you:
- Have several years of experience in technical program management, with a track record of successfully delivering complex technical programs, preferably in AI development, ML engineering, or related fields
- Have experience executing technical programs that require systems and engineering-level knowledge.
- Are comfortable diving deep into technical details of model training while maintaining a high-level view of program status
- Have strong communication skills and can effectively engage with both technical and non-technical stakeholders
- Are familiar with machine learning concepts
- Are highly organized and can manage multiple parallel research workstreams effectively
- Thrive in unstructured environments, and have a knack for bringing order to chaos.
- Have a high threshold for navigating ambiguity and are able to balance setting strategic priorities with rapid, high-quality execution.
- Have a track record of building trust with technical teams and driving change through influence
