Senior / Staff Technical Program Manager, AI Research

Rhoda AI • Mountain View, CA

8h

About The Position

Rhoda AI is hiring a Senior/Staff Technical Program Manager, AI Research, to scale the coordination and execution of research programs. This deeply technical, cross-functional role works across pre-training, post-training, applied research, data, GPU infrastructure, robotic hardware, and customer-facing model delivery. The TPM owns the integrated execution plan across these efforts, connecting milestones, anticipating dependencies, resolving blockers, and helping teams make timely resource and sequencing decisions. The goal is to give researchers and engineers more time to build while making the transition from research experiments to usable models more predictable.

The role partners closely with Research leadership, technical leads, infrastructure, robotics, hardware, and other program leads. It does not own the scientific roadmap or personnel management, but requires enough technical fluency to understand research plans, constructively challenge assumptions, and turn ambiguity into practical options. The TPM will help clarify handoffs and readiness expectations, coordinate constrained resources, establish lightweight execution rhythms, and provide technical leadership with concise, decision-ready updates.

Rhoda AI builds generalist intelligent robots and develops its own robotics stack, including hardware, robot systems, infrastructure, and foundation world models. The role supports the company's capability roadmap and helps coordinate the readiness of models, data, hardware, compute, and evaluation in the right sequence.

Requirements

• Significant technical program management experience delivering complex, interdependent work across software, machine learning, research infrastructure, robotics, or similarly technical organizations; senior- or staff-level judgment.
• Strong understanding of the machine learning development lifecycle, including datasets, model training, evaluation, iteration, and the practical challenges of moving models into production.
• Demonstrated ability to build an execution system from an ambiguous starting point, identify critical dependencies, and drive progress across teams without direct reporting authority.
• Technical fluency to work credibly with researchers and infrastructure engineers, ask relevant questions, and present real trade-offs rather than merely collect status.
• Experience coordinating constrained engineering resources, such as compute capacity, test systems, labs, hardware platforms, or specialized teams.
• Excellent written and verbal communication, including the ability to turn multiple threads of technical work into a concise, decision-ready view for leadership.
• Sound judgment about when to resolve coordination issues independently, involve technical owners, or seek leadership attention.

Nice To Haves

• Experience in large-scale model training, foundation models, generative models, computer vision, embodied AI, or robotics.
• Familiarity with GPU clusters, data pipelines, robot testing and evaluation, or hardware/software integration.
• Experience bridging research organizations and customer-facing applied engineering or model release functions.

Responsibilities

• Build and maintain an integrated research roadmap covering experiments, model and data readiness, system integrations, releases, dependencies, critical paths, and decision points.
• Coordinate transitions from pre-training checkpoints through post-training, applied model engineering, evaluation, and deployment, aligning handoff criteria, owners, and readiness expectations.
• Anticipate and coordinate the availability of training datasets, collection systems, robots, workcells, and GPU capacity; manage resource forecasts and routine scheduling trade-offs within agreed priorities.
• Identify cross-team gaps and blockers early, bring owners together, maintain action plans and contingencies, and escalate consequential trade-offs with concrete recommendations.
• Surface capacity and staffing risks to research leaders, including needs for additional engineering capacity, specialized expertise, or reprioritization; support decisions without owning personnel management.
• Establish lightweight execution rhythms, including focused program reviews, decision and dependency logs, and concise updates for technical leadership.
• Track uncertainty appropriately by distinguishing experiments, proof-of-concept demonstrations, engineering-readiness milestones, and customer-deployment commitments; track confidence and learning goals.
• Partner with hardware and operations TPMs and program leads to clarify interfaces, ownership, acceptance criteria, and release expectations.
• Help improve handoffs, surface blockers early, resolve resource conflicts more quickly, reduce cross-silo coordination burden, and provide leadership with a trustworthy view of milestones and risks.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed