Reflection AI Research Program Manager at Reflection AI to lead cross-functional programs scaling research infrastructure for large-scale training runs. Focuses on coordinating requirements and driving roadmap for training frameworks.
Responsibilities
Research Program Managers at Reflection are high-leverage leaders and operators who embed directly with research and infrastructure teams to accelerate the pace of frontier model development. They are not project trackers. They are force multipliers who bring clarity to ambiguity, drive decisions when the path forward is unclear, and ensure that the work happening across multiple teams connects into a coherent whole.
This role focuses on scaling our research infrastructure to continue to support massive, frontier-scale training runs across pre-training, mid-training, and post-training.
As a central mandate, you will help drive the scaling and continued evolution of our training stack. You will help gather and unify requirements, scope, and drive this scale-up rewrite of the trainer with the Research and GPU Infra engineers who build and operate our training frameworks. This role involves weaving requirements across all stages of our training pipeline: pre-training, mid-training workloads that pair the trainer with a high-throughput inference engine for RL, post-training, etc. We are looking for someone who has gone deep with GPU infrastructure teams to build training frameworks and has accumulated the scar tissue to prove it – you know firsthand which abstractions hold at 10K+ GPU scale and which ones don’t, and you can bring that judgment to a multi-quarter program of a training stack rewrite.
Own cross-functional programs spanning training infrastructure across pre-training, mid-training, and post-training workstreams.
Define and own the roadmap for training frameworks at Reflection
Partner with infrastructure and research engineering leads to identify bottlenecks, define priorities, and ensure that infrastructure investments are directly tied to research velocity.
Build and maintain visibility into training run health so that leadership and teams have the context they need to make fast, informed decisions.
Create lightweight, durable processes for cross-team handoffs, config management, checkpoint workflows, and other coordination-heavy touchpoints that currently rely on ad hoc communication.
Translate technical complexity into clear status updates and decision frameworks for engineering leadership and executives.
Qualification
Track record of managing complexComfortable operating in crisis modeExcited to build from zero to one
Required
7+ years of experience in technical program management, research operations, or infrastructure coordination, ideally in ML/AI or large-scale distributed systems environments.
Deep technical knowledge to engage with engineers on topics like distributed training frameworks, GPU cluster architecture, scheduler behavior, networking, and storage systems. You don't need to write the code, but you need to understand the systems to “speak the language”, i.e., to ask the right questions and identify risks early.
Proven ability to operate effectively in high-ambiguity, fast-moving environments. You create structure where there is none and drive clarity without waiting for permission.
Track record of managing complex, multi-team programs with competing priorities and hard deadlines. You know how to make tradeoffs and you communicate them clearly.
Strong stakeholder management skills across both deeply technical ICs and senior leadership. You build trust by being reliable, direct, and well-informed.
Comfortable operating in crisis mode. You stay calm under pressure, you know how to prioritize when everything is on fire, and you follow through on the other side.
Excited to build from zero to one. We are a small, fast-moving team and this role will help define how Research Program management Works at Reflection.
Motivated by enabling researchers and engineers to build the world's most capable open-weight AI systems.