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1–2 jobs· newest first

AI Research Engineer - Robotics
Helsing GmbH · Barcelona, Spain+1 more
Research Scientist: Post-Training
Generalist · Boston, US

+10K new in the last hour

AI Research Engineer - Robotics

Helsing GmbH · Posted Nov 6, 2026

Barcelona, Catalonia, Spain +1 more

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Lead research and development of reinforcement learning based autonomous robotics solutions, designing, training and deploying RL controllers for real-world robotic systems.

Locations (2)

Barcelona, Catalonia, Spain
London, England, United Kingdom

Compensation

Competitive salary and VSOP options

Responsibilities

Design and train RL-based controllers for autonomous robots
Integrate vision and sensor fusion for robust control
Develop physics-based simulators and transfer policies to hardware
Ensure safety, efficiency and robustness of deployed policies
Collaborate with cross-functional teams on AI infrastructure

Requirements

MSc or PhD in Robotics, Machine Learning, Control Engineering or related field
Nice to have: PhD with publications in top-tier venues
Nice to have: Experience with dynamic robotic systems
Nice to have: Vision-based perception (depth estimation, visual odometry)
Nice to have: Low-level motor control interfaces

Skills

Reinforcement learning Continuous control Model-free methods (PPO, SAC, TD3)Model-based RL Hierarchical RL Sim-to-real transfer Domain randomisation Curriculum learning Robot dynamics and kinematics Classical control theory (PID, MPC)Physics-based simulators (MuJoCo, Isaac Gym, PyBullet, Gazebo)Python Rust or modern C++AI software deployment

Benefits

Competitive salary
VSOP options
Relocation support up to €2,500
4 weeks temporary accommodation
Learning allowance €500/£450 yearly

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Research Scientist: Post-Training

Generalist · Posted Nov 5, 2026

Boston, Massachusetts, United States

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Post‑training research scientist focused on fine‑tuning, reinforcement learning, and evaluation of embodied AI and robotics models.

Responsibilities

Designing fine‑tuning and adaptation strategies for downstream robotic tasks and embodiments
Developing methods for improving reliability, robustness, and controllability
Building evaluation frameworks that measure real‑world robot performance, not just offline metrics
Improving inference‑time performance (latency, stability, memory footprint) in collaboration with ML infrastructure
Leveraging techniques such as imitation learning, RL, distillation, synthetic data, and curriculum learning

Skills

fine‑tuning large models RLHF imitation learning reinforcement learning distillation synthetic data curriculum learning model evaluation

Testing QA monitoring

robustness testing

debugging ML stack

robotic systems

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