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  3. Senior Reinforcement Learning Engineer
Anybotics logo

Senior Reinforcement Learning Engineer

Anybotics
Zurich, Switzerland
Full Time
Posted May 26, 2026
Utilities
~64 people viewed this recently
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Application opens on company website

Job Description

ANYbotics is a fast-growing tech company dedicated to shaping the future of mobile robotics across multiple industries.

Key Responsibilities

  • Lead the design, training, and deployment of reinforcement learning policies for robot motion — bridging the gap from simulation to reliable real-world performance
  • Provide senior technical guidance on RL and learning-based control across the team, mentoring engineers and establishing best practices for policy development workflows
  • Own and evolve the RL training infrastructure and sim-to-real pipeline, ensuring reproducibility, scalability, and fast iteration cycles
  • Shape the technical vision for internal ML tooling and experiment management (e.g. training dashboards, automated evaluation pipelines), driving efficiency and rigour across the team's learning workflows
  • Collaborate closely with cross-functional stakeholders to identify how to expand the robot's autonomous operational envelope
  • Triage field issues related to locomotion, recognise failure patterns, and rapidly improve policy robustness based on real deployment data
  • Write, deploy, and maintain efficient Python and C++ software for the learning and locomotion stack

Requirements

  • PhD in robotics, machine learning, computer science or a related field with a strong focus on reinforcement learning; alternatively, an equivalent track record of RL research and deployment in robotics Or
  • Master's degree from a top-tier technical university (e.g. ETH Zurich, EPFL) in robotics, machine learning, computer science or related field and 5+ years of professional experience
  • Proven track record of shipping ML models to the field and maintaining those solutions over time
  • Solid grounding in robot control fundamentals and autonomous systems, including: motion control, state estimation, path planning and actuation
  • Experience using robotic simulation tools such as Gazebo or Isaac Sim
  • Strong understanding of sim-to-real transfer, domain randomisation, reward shaping, and policy robustness techniques
  • Proficiency in Python and modern ML frameworks (PyTorch); working knowledge of C++
  • Strong knowledge of Linux systems and middleware frameworks for integrating learned components into a larger software stack
  • Pragmatic and solution-oriented mindset — comfortable balancing research exploration with production delivery
  • Excellent communication skills in English

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