Technical Program Manager, T-Labs

Technical Program Manager, T-Labs (R&D)

Location: Trondheim, Norway or San Jose, California

Working arrangement: Primarily in office

About Trener and T-Labs

Trener builds Physical AI for industrial robotics. Our software-defined robotics platform is already running in production on real factory floors through Acteris. Backed by a $32M Series A, we operate from dual headquarters in San Jose, California and Trondheim, Norway, with teammates across six countries.

T-Labs is Trener's R&D group. It takes on the hard, longer-horizon problems that shape what our robots can do next, from learning-based manipulation to the compute that runs those models at the edge, and turns promising research into capabilities our product teams can ship.

The role

As Technical Program Manager for T-Labs, you will be the operational backbone of our R&D group. You will lead program execution across both robot learning and edge compute: coordinating the training and evaluation of robot learning models, bringing those models onto edge AI hardware, and keeping researchers, engineers, hardware partners and product teams aligned on milestones and dependencies.

This is a hands-on, technically fluent role. You will bring depth in at least one of these technical areas and the ability to work effectively across both. You will understand the work well enough to scope programs with technical leads, spot risk early, challenge timelines, and translate between research and production.

You will turn research goals into executable plans, help the team resolve blockers, and keep work moving as priorities and technical understanding evolve. Working closely with technical leads and partners, you will make tradeoffs clear and bring the right people together to make timely decisions.

What you'll do

Run T-Labs programs end to end

• Own planning, dependencies, risks and delivery coordination for multiple concurrent R&D programs, including customer-funded research and partner programs, from early exploration to production handoff.

• Build and maintain roadmaps, dependency maps and risk registers that leadership can trust.

• Run the team's operating cadence: sprint planning, technical reviews, stage gates and status reporting to the VP R&D and executive team.

• Work with technical leads to define experiments, evaluation criteria and decision points; revise plans as evidence changes, and escalate tradeoffs early.

• Define clear handoff criteria with product and engineering teams so research outputs transition into product development with agreed ownership and acceptance criteria.

• Identify capability and staffing gaps with technical leads, and support hiring plans with leadership.

Robot learning and model programs

• Coordinate the full model lifecycle for robot learning work (e.g. imitation learning, reinforcement learning, vision-language-action and foundation models for manipulation): data collection, training runs, evaluation, sim-to-real validation and deployment.

• Plan and track data collection campaigns, teleoperation and demonstration capture, labeling, and dataset versioning.

• Manage GPU and cloud compute capacity, training schedules and budgets with engineering and finance.

• Establish evaluation milestones and success metrics with technical leads so model progress is measurable and informs decisions about what to continue, change or stop.

Chip and edge compute programs

• Drive programs that bring Trener's models onto edge AI hardware (e.g. NVIDIA Jetson-class modules and other AI accelerators), including bring-up, model optimization, benchmarking and integration with our robotics platform.

• Serve as the day-to-day program lead with silicon and hardware partners: joint roadmaps, engineering samples, SDK and toolchain dependencies, and partner deliverables.

• Track performance, latency, power and thermal targets against hardware and product requirements.

Cross-functional and cross-regional coordination

• Connect T-Labs with Product, Operations and Sales so customer needs inform research priorities.

• Coordinate across our San Jose and Trondheim teams and collaborators in other time zones.

• Manage external collaborations, including university partners, research grants and vendor relationships.

What you bring

• 5+ years of technical project or program management, with at least 2 years supporting ML, robotics, semiconductor or other deep-tech R&D teams.

• Technical depth in at least one of the following areas, with the ability to coordinate programs and dependencies across both:
• Robot learning / ML: the model development lifecycle, including data pipelines, training, evaluation and deployment. Enough fluency to discuss tradeoffs with ML researchers.

• Chip / edge compute: AI accelerators, embedded or edge platforms, hardware bring-up, and model optimization (quantization, compilation, inference runtimes).

• A technical degree (CS, EE, ME, Robotics or similar) or equivalent hands-on experience.

• A track record of delivering complex, multi-team projects where requirements shifted as the research evolved.

• Strong command of planning tools and practices: roadmaps, dependency tracking, risk management, and both agile and stage-gate methods.

• Clear, direct communication with researchers, engineers, executives and external partners.

• Comfort with ambiguity and a startup pace: you build the process the team needs and adapt plans as new evidence emerges.

Nice to have

• Experience with robot manipulation or industrial automation.

• Exposure to simulation environments (e.g. Isaac Sim, MuJoCo, Gazebo) and sim-to-real workflows.

• Experience managing relationships with silicon vendors or hardware OEMs.

• Familiarity with ML infrastructure: experiment tracking, GPU cluster scheduling, cloud training.

• Experience with university research partnerships or public R&D grants.

• Experience working across US and European teams.

• PMP, Scrum or similar certification (not required).

What success looks like in your first six months

• Agreed priorities and ownership. Active robot learning and edge compute programs have clear goals, owners, milestones and decision points, aligned with available resources.

• Visible dependencies and timely decisions. The team and leadership can see critical dependencies across data, models, compute, hardware and partners. Risks and resource conflicts are surfaced early enough to act on them.

• Evidence-based research reviews. Technical reviews use agreed evaluation criteria and experiment results to decide what to continue, change or stop, with plans updated as the research evolves.

• A demonstrated research-to-product handoff. At least one agreed research output has been handed over to product or engineering against defined acceptance criteria, with documentation, evaluation results and a named receiving owner.

• An effective operating cadence. Researchers, engineers and partners know what is expected, which decisions are needed and who owns the next step, with a level of process that helps the team deliver.

Why Trener

• Real impact, real factories. Our Physical AI runs in production with customers, not just in demos.

• Shape the next generation of robotics. T-Labs work feeds directly into what our robots learn and how they run.

• A global team. Collaborate daily with colleagues in San Jose, Trondheim and beyond.

• Our values in practice: Move Fast Together, Earn Customer Champions, Deliver with Excellence, Challenge the Possible.

• Competitive package: salary, equity and benefits.

Trener is an equal opportunity employer. We welcome applicants of all backgrounds and evaluate every candidate on their skills and experience.