Reinforcement Learning Lead

Datamentors is a robotics start-up building an agnostic orchestration platform and a family of proprietary robots — quadruped, mobile upper-body and humanoid — assembled at our own factory in Caniçal, Madeira. We also develop custom Vision-Language-Action (VLA) models that give our robots natural-language reasoning and autonomy, deployable across sectors from hospitality and healthcare to defence and logistics. With prototypes running and a clear path to 600 units per year, the engineers joining now will shape both the hardware and the intelligence behind Ardia for years to come. ROLE OVERVIEW Own the post‐training stage of the Ardia humanoid platform — taking pretrained, behaviour‐cloned Vision‐Language‐Action policies and systematically lifting their success rate, precision, and robustness through RL fine‐tuning, reward modelling, and a tight sim‐to‐real loop. This hands‐on leadership role sets the technical direction for RL across the platform, builds the training and evaluation infrastructure, and grows a small team around it. You'll be the person who turns a capable‐but‐inconsistent humanoid into a dependable one — the difference between a research demo and a product. WHAT YOU'LL DO Post-VLA RL fine-tuning — design and run the pipeline that takes a pretrained/imitation-learned VLA policy and improves it with reinforcement learning (online and offline RL, RLHF/RL-from-feedback, preference and reward-model approaches) to lift task success rates and reduce failure modes. Reward design & evaluation — define reward signals, success criteria, and automated evaluation harnesses that actually correlate with real-world manipulation and whole-body performance, and that catch regressions before deployment. Sim-to-real — build and tune the simulation-to-hardware transfer loop (domain randomization, residual policies, real-world fine-tuning) so gains in simulation hold up on the physical robot. Data flywheel — establish the loop that turns robot rollouts and teleop data into better policies: autonomous data collection, filtering, labelling, and continual retraining. Integration across the stack — work with the teams owning the orchestration layer, the VLA policy, and whole-body control so RL improvements compose cleanly rather than fighting the rest of the system. Technical leadership — set the RL roadmap, make build‐vs‐adopt calls on frameworks and tooling, mentor engineers, and represent the work to the wider team and external partners. WHAT WE'RE LOOKING FOR Strong, demonstrable RL expertise: PPO/GRPO and related policy-gradient methods, offline RL, RLHF/preference-based RL, and reward modelling — you understand where each breaks and why. Robot learning / embodied AI experience — ideally hands‐on with VLA models (e.g. OpenVLA, π0, GR00T-class, or comparable manipulation/locomotion policies). Practical sim-to-real experience and fluency with at least one major simulator (Isaac Lab / Isaac Gym, MuJoCo, or similar). Solid ML engineering: PyTorch, distributed training, GPU-efficient pipelines, and the discipline to build reproducible experiments and rigorous evaluation. Evidence of shipping — you've taken a policy from "works in a demo" to "works reliably," not just published benchmarks. Nice to have Direct experience with humanoid or whole-body control (locomotion + manipulation), and with the realities of training on real hardware. Familiarity with VLA post-training specifically — fine-tuning, distillation, or RL on top of large pretrained action models. Comfort working close to perception (vision, point clouds) and to low-level control. Open-source contributions to robot learning or RL frameworks. Experience standing up data-collection / teleoperation pipelines. Publications or applied work at the VLA / robot-learning frontier (ICRA / CoRL / RSS-level). WHY DATAMENTORS Own the post-training layer end to end, with the autonomy to build it your way. The architecture, hardware, and base policies are already in place — your RL work is the leap that turns a capable demo into a dependable product. Hands-on technical leadership: set the RL roadmap, make the tooling calls, and grow a small team around the work. We assess candidates on demonstrated ability, not credentials alone — if you've done the work and can show it, we want to talk. From research demo to dependable product — own it. #J-18808-Ljbffr