Reinforcement Learning Lead

Datamentors; Lisboa; Presencial; EUR 90 000 - 130 000; Tempo integral

Resumo da oferta

Datamentors seeks an Reinforcement Learning Lead to own the post-training stage of the Ardia humanoid platform, lifting success, precision and robustness through RL fine-tuning, reward modelling and a tight sim-to-real loop. You will set the technical direction, build training/evaluation infra, and grow a small team around it.

You will lead the RL roadmap, mentor engineers, and collaborate with perception and control teams to turn demonstrations into a dependable product.

Qualificações

• Strong, demonstrable RL expertise: PPO/GRPO and related policy-gradient methods, offline RL, RLHF/preference-based RL, and reward modelling.
• Robot learning / embodied AI experience - ideally hands-on with VLA models.
• Practical sim-to-real experience and fluency with major simulators (Isaac Lab / Isaac Gym, MuJoCo).
• Solid ML engineering: PyTorch, distributed training, GPU-efficient pipelines, reproducible experiments.

Responsabilidades

• 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.

Conhecimentos

RL expertise

Embodied AI

Sim-to-real

PPO/GRPO

Reward modelling

Ferramentas

PyTorch

Isaac Gym

MuJoCo

GPUs & distributed

Descrição da oferta de emprego

About Datamentors Datamentors is building the next generation of autonomous robotics through a combination of advanced AI, robotics, and proprietary Vision-Language-Action (VLA) models. Our mission is to create intelligent robotic systems capable of understanding natural language, reasoning about the world, and acting autonomously in complex environments.

We are assembling a world-class engineering team to tackle some of the hardest challenges in robotics, AI, perception, and autonomy. The engineers who join us today will have a direct impact on the core technologies that power Ardia and the future of intelligent machines

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.

Responsabilities

• 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.

Requirements

• 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 reliable," 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.

Reinforcement Learning Lead • Funchal, Portuguese Republic, PT