Reinforcement Learning Engineer

DeepRec.ai; Zürich; Hybrid; CHF 120.000 - 180.000; Vollzeit

Zusammenfassung

DeepRec.ai in Zurich is hiring a Senior Reinforcement Learning Engineer to advance learning-based planning and control for autonomous excavators and robots across sites. You will move RL from simulation into real machines, working with Python, PyTorch and C++ in a hands-on role within our hybrid Zurich office.

You’ll collaborate with the autonomy team to improve sim-to-real transfers, build data pipelines for real-world training, run experiments on hardware, and help deliver a long-lived system

Qualifikationen

• 2–5 years’ industry RL experience in control or planning.
• Proven deployment on physical robots.
• Strong Python and PyTorch, plus good C++.
• Experience with simulation and sim-to-real transfer.

Aufgaben

• Develop learning-based planning and control systems that work outside the simulator.
• Improve simulation and sim-to-real transfer pipelines.
• Run experiments on physical machines and diagnose real-world behaviours.
• Integrate learned components into the autonomy stack and shape product readiness.

Kenntnisse

Python

PyTorch

C++

RL

Tools

Python

C++

GPU-accelerated simulation

Simulation

Jobbeschreibung

Zurich | Hybrid | Full-time You've already deployed reinforcement learning on real robots. Now you can apply that experience to autonomous excavators working across different machines, sites and soil conditions. You'll join a Series A robotics company taking Physical AI into construction, with systems already deployed across multiple countries.

Senior Reinforcement Learning Engineer

Zurich | Hybrid | Full-time You've already deployed reinforcement learning on real robots. Now you can apply that experience to autonomous excavators working across different machines, sites and soil conditions. You'll join a Series A robotics company taking Physical AI into construction, with systems already deployed across multiple countries.

You’ll build learning-based planning and control systems that work outside the simulator. That means improving simulation and sim-to-real transfer, designing data pipelines for real-world training, running experiments on physical machines and understanding why behaviour changes when conditions get messy. You’ll also integrate learned components into the wider autonomy stack and help shape how the system moves from prototype into a reliable product.

This is a hands-on engineering role for someone with 2–5 years of industry experience in reinforcement learning for control or planning, who has actually deployed systems on physical robots. You’ll need strong Python and PyTorch skills, good C++, experience with GPU-accelerated simulation, and the ability to debug real-world robotic behaviour. Experience with hydraulic machinery, large-scale deployments, imitation learning or production rollout strategies would be useful.

You’ll have genuine scope to influence the technical direction as the autonomy team builds a long-lived system designed to operate across the construction industry. If you want your RL work to move from simulation into machines doing real work, this is an opportunity to do exactly that.

You’ll need:

• 2–5 years’ industry RL experience in control or planning
• Proven deployment on physical robots
• Strong Python/PyTorch and good C++
• Experience with simulation and sim-to-real
• Willingness to travel when projects require it

If the challenge fits your background, let’s have a conversation about the role and the problems you’d be working on.