Machine Learning Engineer - Sim2Real & Machine Modeling

Switzerland·ZürichFull timemid

Machine Learning EngineerData

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Overview

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.

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Machine Learning EngineerData

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.

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.

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

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

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

~1 min read

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

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.