Technical Program Manager, Research

Luma AI•Palo Alto, CA

15h

About The Position

We’re looking for a Technical Program Manager to partner closely with researchers and engineers building state-of-the-art generative AI video models. In this role, you’ll help turn cutting-edge research into scalable, reliable systems by driving execution across research, infrastructure, and product engineering. You will operate at the intersection of research, systems, and delivery, helping teams plan, prioritize, and execute complex technical programs while preserving the exploratory nature of research.

Requirements

5+ years of experience in Technical Program Management, Engineering Program Management, or similar role
Strong technical background with the ability to engage deeply with: Machine learning concepts (especially deep learning), Large-scale training and experimentation workflows, Distributed systems or ML infrastructure
Experience working directly with researchers or research-adjacent teams
Proven ability to manage ambiguous, fast-evolving technical programs
Excellent communication skills — able to align highly technical stakeholders

Nice To Haves

Experience with generative models, especially video, vision, or multimodal systems
Familiarity with: Model training at scale (multi-node, multi-GPU), Data versioning, experiment tracking, and evaluation frameworks, ML deployment and inference optimization
Background in computer science, engineering, or a related technical field
Experience in AI-first or research-driven organizations

Responsibilities

Partner with research scientists, ML engineers, and infrastructure teams to plan and deliver programs for generative video model development
Translate research goals into clear technical milestones, timelines, and dependencies
Drive execution across the full lifecycle: experimentation → training → evaluation → scaling → deployment
Coordinate cross-functional efforts spanning: Model training and evaluation, Data pipelines and curation, Compute planning (GPU/TPU usage, scheduling, cost awareness), Inference optimization and deployment
Create lightweight but effective program artifacts (roadmaps, risk registers, decision logs)
Identify risks early (technical, resourcing, compute, data) and proactively drive mitigations
Improve operational rigor without slowing down research velocity
Act as a connective tissue between research, product, and platform teams
Help define and evolve best practices for running large-scale AI research programs