Our MissionAt 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 ROLEAs 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 ONTrain 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 t.