Senior Machine Learning Engineer - Sim2Real & Machine Modeling

Carbon Data Solutions; Zürich; Vor Ort; CHF 110.000 - 170.000; Vollzeit

Zusammenfassung

Gravis Robotics is hiring for an autonomy-focused role that strengthens the sim2real gap for autonomous controllers in heavy machinery. You will develop ML models, assess architectures, and work closely with autonomy and simulation teams to influence on-machine controllers.

The ideal candidate has a strong ML/RL background, experience with real robotic systems, and solid Python/PyTorch skills. A degree in CS/Robotics/Engineering is required, and a hands-on, data-driven approach is valued.

Qualifikationen

• Degree in Computer Science, Robotics, Machine Learning, Engineering, or related field.
• Strong Python and PyTorch, and git skills.
• Solid experience modeling time-series or dynamical-system data from large datasets.
• Analytical mindset: design experiments, run ablations, draw defendable conclusions.

Aufgaben

• Build ML models to bridge the sim2real gap for autonomous controllers.
• Decide and justify architectures (sequence models, state-space, etc.) with data.
• Characterize unmodeled effects and data distributions needed.
• Define performance metrics and validation for model fidelity and sim2real transfer.

Kenntnisse

Python

PyTorch

Git

Time-series modeling

Robotics

Ausbildung

Bachelor’s/Master’s in CS/Robotics/Engineering

Jobbeschreibung

Gravis Robotics is a high-growth Series A start-up backed by SoftBank, bringing Physical AI to the construction industry, turning heavy construction machines into autonomous robots.

Gravis began as an ETH Zurich spin-out, and our unique combination of learning-based automation and augmented remote control lets one operator safely conduct a fleet of earthmoving machines in a gamified environment.

Backed by deep robotics research and now deployed across multiple countries with leading construction and equipment partners, our team is rapidly growing to bring this technology to a trillion-dollar industry.

The Gravis RACK is a machine-agnostic retrofit kit that adds autonomy to excavators and wheel loaders from 10 to 100+ tonnes: LiDAR and camera sensing, GNSS RTK, networking hardware and rugged edge compute that works offline. Paired with the Slate tablet and our Copilot software, it lets an operator run a machine manually, with AI assistance, or fully autonomously. Increasingly, we also build custom hardware to adapt our machines for highly specialized, robust applications beyond traditional excavation.

About the Role

Autonomy team at Gravis heavily relies on simulation to develop autonomous controllers. Whether these controllers work on the machine depends on how well we close the sim2real gap. In this role you will help us bridge the gap. We are looking for someone with strong ML/RL background and experience with real robotic systems.

What you will do

Machine & dynamics modeling

• Build ML models to help bridge the sim2real gap
• Decide what architecture the problem actually needs - sequence models, state-space formulations, something else - and back the choice with data
• Characterize where the gap actually comes from: which unmodeled effects hamper the sim2real transfer, and which we can safely ignore
• Answer how much data is needed and what distribution it has to cover

Performance monitoring

• Define the performance metrics and validation methodology for model fidelity and sim2real transfer
• Build models and methods that detect machine properties changing over time
• Work closely with the autonomy and simulation teams — your models influence the controllers that run on the machine

What we're looking for

Required

• Degree in Computer Science, Robotics, Machine Learning, Engineering, or a related field
• Strong Python and PyTorch, strong git skills
• Solid experience modeling time-series or dynamical-system data from large datasets - sequence models, system identification, or state-space approaches
• Strong analytical skills: you design the experiment, run the ablation, and draw a conclusion you'd defend

Nice to have

• Reinforcement learning experience
• Imitation learning or learning from demonstration, especially from human operator data
• Familiarity with recent literature and methods in learned behavior policies
• Classical system identification, control, or hydraulics background

This role is a great fit if…

• You like to solve problems outside of the laboratory
• You like a culture where the best idea wins no matter whether it comes from the CTO or an intern, as long as it's backed by numbers
• You are comfortable owning the result end to end: when the data you need doesn't exist yet, you go on site, touch the machine and get it
• You'd take a simple model that measurably closes the gap over a sophisticated one that might, and you're patient enough to get there in steps

This is an opportunity to join a dynamic, multidisciplinary team and to be part of a company that is reshaping heavy construction.

Gravis is an equal opportunity employer. We are committed to building an inclusive and diverse team, and do not discriminate based on race, colour, ancestry, national origin, religion, sex, sexual orientation, age, gender identity, gender expression, disability, veteran status, or other legally protected characteristics.