Job description

At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality. THE ROLE We're looking for a Senior/Staff Technical Program Manager, AI Research to help scale how research programs come together at Rhoda. This is a deeply technical, cross-functional execution role at the intersection of pre-training, post-training, applied research, data, GPU infrastructure, robotic hardware, and customer-facing model delivery. You'll own the integrated execution plan across these efforts: connecting milestones, anticipating dependencies, driving blockers to resolution, and helping teams make timely resource and sequencing decisions. The objective is to give researchers and engineers more time to build, while making the path from research experiments to usable models more predictable. You'll work closely with Research leadership, technical leads, infrastructure, robotics, and hardware teams. You won't need to invent the scientific roadmap yourself—but you should be technical enough to understand it, challenge assumptions constructively, and turn ambiguity into practical options. WHAT YOU'LL DO - Build the integrated research roadmap. Translate individual project plans into a clear, continuously updated view of experiments, model and data readiness, system integrations, releases, dependencies, critical paths, and decision points. - Connect model development to downstream use. Coordinate the transitions from pre-training checkpoints to post-training, applied model engineering, evaluation, and deployment; align on handoff criteria, owners, and readiness expectations. - Synchronize data, compute, and hardware. Anticipate when training datasets, collection systems, robots, workcells, and GPU capacity need to be available; manage resource demand forecasts and routine scheduling trade-offs within agreed priorities. - Drive blockers to resolution. Identify cross-team gaps before they become delays, bring the right owners together, maintain action plans and contingencies, and escalate consequential trade-offs with a concrete recommendation. - Surface capacity and staffing risks early. Help research leaders understand where milestones need additional engineering capacity, specialized expertise, or reprioritization; support decisions without owning personnel management. - Build lightweight execution rhythms. Run focused program reviews, maintain decision and dependency logs, and create concise, reliable updates for technical leadership without burying researchers in process. - Manage uncertainty intelligently. Distinguish experiments, proof-of-concept demonstrations, engineering-readiness milestones, and customer-deployment commitments; track confidence and learning goals instead of forcing speculative research into false precision. - Improve cross-functional execution. Partner with hardware and operations TPMs and program leads to clarify interfaces, ownership, acceptance criteria, and release expectations as Rhoda scales. WHAT WE'RE LOOKING FOR - Significant technical program management experience delivering complex, interdependent work across software, ML, 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 true critical dependencies, and drive progress across teams without direct reporting authority. - Technical fluency sufficient to work credibly with researchers and infrastructure engineers, ask the right questions, and present real trade-offs—not just 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 many threads of technical work into a concise, decision-ready view for leadership. - Sound judgment about when to solve a coordination issue independently, when to involve technical owners, and when a decision needs leadership attention. NICE TO HAVE - 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. WHY THIS MATTERS Rhoda's capability roadmap spans faster task adaptation, instruction and demonstration following, higher execution speed, and dexterous manipulation. These breakthroughs depend on models, data, hardware, compute, and evaluation becoming ready in the right sequence. You'll help turn that complexity into a coherent execution engine—without slowing down the research itself. Success in this role means important handoffs are clear, blockers surface early, resource conflicts are resolved faster, technical teams spend less time coordinating across silos, and leadership has a trustworthy view of milestones and risks.

Requirements

Must-haves

Build lightweight execution rhythms. Run focused program reviews, maintain decision and dependency logs, and create concise, reliable updates for technical leadership without burying researchers in process.
Improve cross-functional execution. Partner with hardware and operations TPMs and program leads to clarify interfaces, ownership, acceptance criteria, and release expectations as Rhoda scales.
Significant technical program management experience delivering complex, interdependent work across software, ML, 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 true critical dependencies, and drive progress across teams without direct reporting authority.
Technical fluency sufficient to work credibly with researchers and infrastructure engineers, ask the right questions, and present real trade-offs—not just 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 many threads of technical work into a concise, decision-ready view for leadership.
Sound judgment about when to solve a coordination issue independently, when to involve technical owners, and when a decision needs 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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learning

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Vision