Our Mission

At Laelaps AI, we believe robotics is entering a transformative decade, much like the arrival of the internet. Advances in AI, cloud computing, and hardware are reshaping what autonomous systems can do. Our mission is to build the intelligent software that powers physical security in the real world - enabling robots and sensors to handle dangerous and critical tasks that humans shouldn't have to. By engineering the orchestration layer for intelligent security, we aim to create a world that is safer, more secure, and more resilient.

We're a strong founding team based in Zurich, backed by visionary investors and advisors. We are engineering the future of security today!

THE ROLE

As a Reinforcement Learning Intern on Robot Autonomy, you'll train locomotion and low-level control policies for our legged security robots and help take them from simulation to real hardware. You'll work close to the actuators, from joint-level behavior through coordinated locomotion, on robots that patrol outdoor sites across varied terrain and challenging weather.

This role is highly practical: you'll design training setups, run experiments in simulation, transfer policies to physical robots, and measure how they hold up. You'll get hands-on experience with the gap between a policy that works in simulation and one that keeps a robot on its feet on wet ground at night.

WHAT YOU'LL WORK ON

• Train and evaluate reinforcement learning policies for locomotion and low-level control in simulation.

• Apply sim-to-real techniques such as domain randomization, reward design, and policy robustness methods, and test the results on physical robots.

• Make policies robust to varied terrain, challenging weather, and noisy, delayed, or missing sensor data.

• Explore control approaches that transfer across robot embodiments with different actuators and dynamics.

• Build evaluation workflows with clear metrics and repeatable experiments, in simulation and on hardware.

• Apply solid engineering practices: experiment tracking, version control, reproducible training runs.

WHO WE'RE LOOKING FOR

We're looking for a motivated robotics or machine learning student excited to apply academic training in a fast-moving startup. You'll be surrounded by a team that values learning, experimentation, and building things that actually work in the real world.

YOUR BACKGROUND:

• Currently pursuing a PhD or recently completed a Master's degree in Robotics, Machine Learning, Computer Science, or a closely related field.

• Hands-on experience training reinforcement learning policies for robot control, in simulation or on hardware.

• Experience with robotics simulators such as Isaac Sim, MuJoCo, or Gazebo.

• Solid grounding in robot dynamics and control.

• Good coding skills in Python, with PyTorch (or JAX).

• Comfortable using Docker and Git in your workflows.

NICE TO HAVE:

• Experience deploying learned policies on physical robots, ideally legged.

• Experience with low-level actuator, motor, or joint control.

• C++ and ROS 2 experience.

• Publications at top robotics or ML venues (CoRL, RSS, ICRA, NeurIPS).

What We Offer

• Ownership: you are able to ship products and deliver project end-to-end.

• Mission: autonomous security that keeps people and critical sites safe, including in defence.

• Career path: a ground-floor seat with real runway. Prove your value and you will not have barriers to grow.

• Team: work directly with PhD-level co-founders in AI, Robotics, and Physics, alongside a strong (and fun) founding team.

• Compensation: Competitive equity/salary package

• Culture: International founding team that is serious about building but does not take itself too seriously.

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