Research Program Manager - Model Development
A research program manager embedded with Reflection's pre-training ML and Data teams, coordinating frontier LLM development — experiment planning, data pipelines, run scheduling, and cross-team handoffs. Day to day, they build lightweight processes and drive decisions so researchers can move faster on open model development.
- Compensation
- Not disclosed
- Location
- London, United Kingdom
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 7+ years
- Category
- Project management
- Posted
Skills
Must have
- 7+ years of experience in technical program management, research operations, or ML engineering
- Track record of building programs from scratch in research or ML-heavy environments
- Deep enough technically to engage with researchers on model architecture, scaling laws, data processing pipelines, training dynamics, and optimizer behavior
- Proven ability to embed within technical teams and earn trust through competence, reliability, and curiosity
- Resourceful and high-agency; navigates ambiguity and shifting priorities without losing momentum
- Strong stakeholder management; influence senior technical staff through delivery and judgment
- Comfortable in high-stakes environments impacting model quality, compute spend, and training timelines
Nice to have
- Excited to build from zero to one in a small, fast-moving team
- Motivated by enabling researchers and engineers to build open-weight AI systems
Full description
Our Mission
Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.
About the Role
Research Program Managers at Reflection are high-leverage leaders and operators who embed directly with research and engineering 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 is embedded directly within our Pre-training ML and Data teams, working alongside our Pre-training research leads at the core of how we build frontier models. You will be deeply involved in the research lifecycle, from data pipeline coordination and experiment planning to model architecture decisions and scaling strategies. Your job is to understand how our researchers work, identify the highest-leverage gaps, and build the programs, processes, and coordination structures that let them focus on pushing the frontier rather than navigating organizational complexity.
You bring a first-responder mentality. When things go sideways, you don't wait to be asked. You jump in, assess the situation, cut through noise, align the people who need to be aligned, and drive resolution.
What You'll Do
- Embed within the pre-training ML and Data teams to deeply understand the technical landscape, build trust with researchers and technical leads, and identify where programs and process can have the most impact on research velocity.
- Drive end-to-end execution of complex, cross-team research initiatives that span data, model architecture, training runs, and evaluation, often without established playbooks.
- Coordinate the operational rhythm of pre-training research, including experiment prioritization, run scheduling, data readiness, and checkpoint handoffs to downstream teams.
- Equip research leadership to make decisions quickly by going deep on technical tradeoffs and presenting clear, actionable recommendations rather than status updates.
- Build lightweight processes that bring structure to unstructured research environments without adding friction. Create coordination mechanisms for cross-team handoffs, config management, and research milestones that replace ad hoc Slack threads with durable, visible systems.
- Act as the connective tissue between pre-training, mid-training, post-training, and infrastructure teams, ensuring that upstream decisions propagate cleanly and downstream teams are never surprised.