{"id":3994544,"url":"https://alion.io/job/trener-robotics-technical-program-manager-t-labs","title":"Technical Program Manager, T-Labs","company":{"id":685055,"name":"Trener Robotics","domain":"trener.ai","url":"https://alion.io/company/trener","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Product","role_family":"Product","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Trondheim, Norway"],"countries":["NO"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Edge AI","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Physical AI","optional":false},{"name":"Quantization","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Teleoperation","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"Gazebo","optional":true},{"name":"Isaac Sim","optional":true},{"name":"MuJoCo","optional":true},{"name":"Scrum","optional":true}],"status":"live","first_seen_at":"2026-11-09T20:24:45Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-10T17:02:25Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Technical Program Manager, T-Labs (R&D)\nLocation: Trondheim, Norway or San Jose, California\nWorking arrangement: Primarily in office\nAbout Trener and T-Labs\nTrener 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.\nT-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.\nThe role\nAs 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.\nThis 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.\nYou 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.\nWhat you'll do\nRun T-Labs programs end to end\nOwn 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.\n\nBuild and maintain roadmaps, dependency maps and risk registers that leadership can trust.\n\nRun the team's operating cadence: sprint planning, technical reviews, stage gates and status reporting to the VP R&D and executive team.\n\nWork with technical leads to define experiments, evaluation criteria and decision points; revise plans as evidence changes, and escalate tradeoffs early.\n\nDefine clear handoff criteria with product and engineering teams so research outputs transition into product development with agreed ownership and acceptance criteria.\n\nIdentify capability and staffing gaps with technical leads, and support hiring plans with leadership.\n\nRobot learning and model programs\nCoordinate 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.\n\nPlan and track data collection campaigns, teleoperation and demonstration capture, labeling, and dataset versioning.\n\nManage GPU and cloud compute capacity, training schedules and budgets with engineering and finance.\n\nEstablish evaluation milestones and success metrics with technical leads so model progress is measurable and informs decisions about what to continue, change or stop.\n\nChip and edge compute programs\nDrive 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.\n\nServe as the day-to-day program lead with silicon and hardware partners: joint roadmaps, engineering samples, SDK and toolchain dependencies, and partner deliverables.\n\nTrack performance, latency, power and thermal targets against hardware and product requirements.\n\nCross-functional and cross-regional coordination\nConnect T-Labs with Product, Operations and Sales so customer needs inform research priorities.\n\nCoordinate across our San Jose and Trondheim teams and collaborators in other time zones.\n\nManage external collaborations, including university partners, research grants and vendor relationships.\n\nWhat you bring\n5+ years of technical project or program management, with at least 2 years supporting ML, robotics, semiconductor or other deep-tech R&D teams.\n\nTechnical depth in at least one of the following areas, with the ability to coordinate programs and dependencies across both:\nRobot learning / ML: the model development lifecycle, including data pipelines, training, evaluation and deployment. Enough fluency to discuss tradeoffs with ML researchers.\n\nChip / edge compute: AI accelerators, embedded or edge platforms, hardware bring-up, and model optimization (quantization, compilation, inference runtimes).\n\nA technical degree (CS, EE, ME, Robotics or similar) or equivalent hands-on experience.\n\nA track record of delivering complex, multi-team projects where requirements shifted as the research evolved.\n\nStrong command of planning tools and practices: roadmaps, dependency tracking, risk management, and both agile and stage-gate methods.\n\nClear, direct communication with researchers, engineers, executives and external partners.\n\nComfort with ambiguity and a startup pace: you build the process the team needs and adapt plans as new evidence emerges.\n\nNice to have\nExperience with robot manipulation or industrial automation.\n\nExposure to simulation environments (e.g. Isaac Sim, MuJoCo, Gazebo) and sim-to-real workflows.\n\nExperience managing relationships with silicon vendors or hardware OEMs.\n\nFamiliarity with ML infrastructure: experiment tracking, GPU cluster scheduling, cloud training.\n\nExperience with university research partnerships or public R&D grants.\n\nExperience working across US and European teams.\n\nPMP, Scrum or similar certification (not required).\n\nWhat success looks like in your first six months\nAgreed priorities and ownership. Active robot learning and edge compute programs have clear goals, owners, milestones and decision points, aligned with available resources.\n\nVisible 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.\n\nEvidence-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.\n\nA 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.\n\nAn 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.\n\nWhy Trener\nReal impact, real factories. Our Physical AI runs in production with customers, not just in demos.\n\nShape the next generation of robotics. T-Labs work feeds directly into what our robots learn and how they run.\n\nA global team. Collaborate daily with colleagues in San Jose, Trondheim and beyond.\n\nOur values in practice: Move Fast Together, Earn Customer Champions, Deliver with Excellence, Challenge the Possible.\n\nCompetitive package: salary, equity and benefits.\n\nTrener is an equal opportunity employer. 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