Software Engineer, Robot Autonomy (Multi Agent Orchestration), Intern

Laelaps AI Ltd.; Zürich; On-site; CHF 22,000 - 36,000; Full time

Benefits offered by this job

Ownership of projects

Ambitious mission in autonomous safety

Strong growth runway

PhD-level mentorship from co-founders

Competitive equity/salary package

Job summary

Laelaps AI Ltd in Zurich is seeking a Software Engineering Intern specializing in Multi-Agent Orchestration to advance the orchestration layer for autonomous agents coordinating across security tasks. You will work on MARL, decentralized planning, and real-world deployment on quadruped robots, with strong mentorship from PhD-level co-founders.

Ideal candidates are in final master's or PhD in CS/Robotics/AI, with experience in reinforcement learning, Python and PyTorch, and hands-on robotics

Qualifications

• Currently in the final stages of a Master’s degree or pursuing a PhD in Computer Science, Robotics, AI or a related field.
• Demonstrated hands-on experience with Reinforcement Learning.
• Experience with robotics simulators such as Isaac Sim, MuJoCo, or Gazebo or custom built simulators.
• Solid understanding of reinforcement learning algorithms and concepts.
• Ability to independently research, implement, and evaluate algorithms.
• Good coding skills in Python, with PyTorch (or JAX).

Responsibilities

• Multi-Agent Reinforcement Learning: Design, train, and evaluate MARL algorithms for cooperative multi-robot surveillance and persistent patrolling.
• Multi-Robot Coordination: Develop strategies for decentralized decision-making, task allocation, and efficient collaboration between autonomous robots.
• Simulation: Improve our simulations to train the policies that can be later transferred to the real world.
• Real-World Deployment: Build and test algorithms in simulation environments, with opportunities to deploy them on real quadruped robots.
• Algorithm Research & Optimization: Explore and compare learning-based approaches with classical optimization, planning, and heuristic methods.
• Performance Evaluation: Develop benchmarks to assess surveillance coverage, patrol efficiency, scalability, and robustness in dynamic environments.

Skills

Reinforcement Learning

Python

PyTorch

JAX

Education

Master’s degree in Computer Science, Robotics, AI (final stages)

PhD candidate in Computer Science/Robotics/AI

Tools

Isaac Sim

MuJoCo

Gazebo

Custom simulators

Job description

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.

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 Software Engineering Intern in Multi-Agent Orchestration, you'll work on developing the orchestration layer that enables our autonomous agents to coordinate, collaborate, and make intelligent decisions to achieve efficient and scalable surveillance. Your primary focus will be on Multi-Agent Reinforcement Learning (MARL), exploring how agents can learn optimal coordination strategies in dynamic environments. You'll also have the opportunity to explore alternative approaches, including task allocation algorithms, decentralized planning, and optimization techniques, to build robust and adaptive multi-agent systems. This role is highly practical as we expect the developed methodologies not only to work I. simulation, but be deployed in real world scenarios.

What You'll Work On

• Multi-Agent Reinforcement Learning: Design, train, and evaluate MARL algorithms for cooperative multi-robot surveillance and persistent patrolling.
• Multi-Robot Coordination: Develop strategies for decentralized decision-making, task allocation, and efficient collaboration between autonomous robots.
• Simulation: Improve our simulations to train the policies that can be later transferred to the real world.
• Real-World Deployment: Build and test algorithms in simulation environments, with opportunities to deploy them on real quadruped robots.
• Algorithm Research & Optimization: Explore and compare learning-based approaches with classical optimization, planning, and heuristic methods.
• Performance Evaluation: Develop benchmarks to assess surveillance coverage, patrol efficiency, scalability, and robustness in dynamic environments.

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 in the final stages of a Master's degree or pursuing a PhD in Computer Science, Robotics, Artificial Intelligence, or a related field.
• Demonstrated hands-on experience with Reinforcement Learning, backed by academic, personal, or research projects.
• Experience with robotics simulators such as Isaac Sim, MuJoCo, or Gazebo or custom built simulators.
• Solid understanding of reinforcement learning algorithms and concepts.
• Ability to independently research, implement, and evaluate algorithms.
• Good coding skills in Python, with PyTorch (or JAX).

Nice to Have

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