Technical Program Manager, AI Research - CA, Santa Clara, United States

Originally posted 23 September 2026 by the employer.

Advance reinforcement learning and post training, generalization across hardware engineering contexts, and the RL infrastructure.

About the role

This role drives execution across AI Research scientists and research infrastructure engineers. The focus is on advancing reinforcement learning and post training, generalization across hardware engineering contexts, and the RL infrastructure.

What you'll do

• Own research program planning: quarterly and annual milestones, experiment roadmaps, publication or technical report timelines, and dependency tracking across research pillars.
• Coordinate research execution with multiple AI teams on datasets, eval harnesses, compute allocation, and method landing criteria.
• Maintain research operating cadence: milestone reviews, experiment readouts, decision logs, and escalation of scientific or infrastructure blockers.
• Track progress against research goals using clear metrics: benchmark movement, ablation completeness, reproducibility, stability, generalization evidence, and safety or governance checkpoints for RSI pilots.
• Manage compute planning and experiment throughput: GPU job scheduling assumptions, cluster capacity negotiations, and prioritization across competing research threads.
• Prepare executive and leadership updates that explain scientific progress, uncertainty, risks, and recommended investment shifts in plain language.

What you'll need

• Experience translating complex AI research into clear plans, risks, and priorities.
• Strong program discipline: milestone tracking, dependency management, decision records, and concise status synthesis.
• Familiarity with Reinforcement learning and post training for LLMs, code generation models, and agentic workflows.
• Benchmark design and process to evaluate AI models on hardware engineering tasks.
• Familiarity with Large scale infrastructure: distributed training, rollout workers, logging, checkpointing.
• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience.

Nice to have

• Experience managing ambiguous, experiment driven programs.
• Technical program management, research program management, or equivalent experience in AI, ML, or large scale compute organizations.
• Fluency in machine learning, reinforcement learning, LLM post training, or ML systems.
• Experience supporting AI research labs, and ML R&D organizations.
• Familiarity with publication cycles, internal tech report processes, and research into engineering handoff practices.
• Exposure to RLHF, GRPO, preference optimization, reward modeling, or large-scale experiment management.
• Experience with GPU cluster planning and distributed training workflows.
• Familiarity with hardware design, verification, EDA, or hardware/software co-design environments.

Skills: AI Research, reinforcement learning, research infrastructure, program planning, cross-functional teams, GPU cluster planning