About the role

Technical Program Manager driving scientific execution and coordination at Mistral AI. Overseeing projects and strategic planning within AI-focused science operations team.

Responsibilities

Execution & Release Operations: Design and own systems for tracking ongoing pretraining and posttraining releases - providing real-time visibility into readiness, trade-offs, and impact. Evolve ad-hoc efforts into systematic release operations that enable disciplined, high-velocity execution.
Strategic Planning & Roadmapping: Partner with science leads to translate research goals into actionable, company-aligned roadmaps. Frame scientific capabilities in business-relevant terms and ensure that priorities are consistently communicated and understood across teams.
Operational Unblocking & Coordination: Keep science moving - anticipate and clear roadblocks, streamline cross-functional coordination, and maintain momentum. Own GPU allocation and quota management to ensure compute resources are scaled and accessible when needed.
Visibility & Communication Cadence: Drive structured communication loops - own the bi-weekly science updates, science all-hands, and company-wide digests. Synthesize inputs into clear, high-signal narratives that inform leadership and align the broader organization.
Learning & Continuous Improvement: Run retrospectives after major sprints and releases, distilling insights into repeatable systems. Refine processes, templates, and feedback loops to strengthen organizational learning and operational excellence over time.

Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent practical experience.
3+ years of experience in technical program management, operations, or research coordination.
Strong analytical and problem-solving skills with experience in structuring ambiguous projects into clear deliverables.
Proven ability to drive cross-functional initiatives and manage complex stakeholder landscapes.
Excellent written and verbal communication skills; ability to synthesize technical detail into clear, strategic updates.
Nice to have: Experience in research-heavy or ML/AI organizations.
Familiarity with model development pipelines (pretraining, posttraining, release readiness).
Experience managing infrastructure workflows (API keys, quota, access).
Track record of building systemic processes from the ground up.
Comfort working in fast-paced, high-ambiguity environments where priorities shift quickly.