7 beste Stellen als machine learning engineer in zürich zh – aktuell auf Personalsuche

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Simulation Lead Engineer

Gravis Robotics
Zürich, ZH

Jetzt bewerben

Details zur Stelle

100% | Pensum: 90-100%

Qualifikationen

CI/CD
Englisch
Master
AWS
C++
Bachelor
GitHub
Robotik
Kommunikationsfähigkeit
Python

Vollständige Stellenbeschreibung

Gravis Robotics is a startup turning heavy construction machines into intelligent and autonomous robots. Our unique combination of learning-based automation and augmented remote control enables a single operator to safely manage a fleet of earthmoving machines in a gamified environment. With over a decade of academic experience at the cutting edge of large-scale robotics, our team is rapidly translating this expertise into real-world deployments with industry leaders in a trillion-dollar market.

About the job

At Gravis, we operate at the intersection of hardware, software, and real-world deployment. Our Rooftop Autonomous Control Kit (RACK) integrates sensing, compute, communication, and networking into a manufacturer-agnostic solution deployable across a wide range of construction machines.

As the Gravis Simulation Lead Engineer, you will own the entire simulation landscape at Gravis. You'll define the architecture, implement it, set the strategy, and build the infrastructure that powers simulation-based software testing, automated regression testing, ML policy training, and synthetic data generation across our Autonomy, Perception, and Platform teams. This is a high-impact role at the intersection of all our deep technical disciplines. We expect this role to quickly grow into a technical team lead position.

Responsibilities

Own and drive the roadmap for Gravis's simulation platform: architecture, implementation, tooling, infrastructure, and long-term strategy
Build and maintain high-fidelity simulation environments in GPU-accelerated simulation tailored to heavy construction machinery: terrain, sensors, hydraulics, soil dynamics, …
Design and implement automated simulation testing, regression pipelines, and CI integration
Develop synthetic data generation and domain randomization pipelines to support ML policy training and perception model development
Actively close the sim-to-real gap: instrument & analyse simulation/real-world mismatches
Collaborate with Autonomy, Perception, and Platform teams to gather and translate requirements into simulation capabilities to enable them in their mission
Build and manage the compute infrastructure to run simulation workloads continuously at scale (in-house and cloud-based)
Recruit and technically lead simulation engineers

Required Qualifications

Bachelor's or Master's in Computer Science, Robotics, or a closely related field
5+ years of experience in robotics simulation or a related field, or results that make years of experience irrelevant
Hands-on experience with GPU-accelerated simulation software
Strong software engineering skills in Python. We expect production-quality code
Experience integrating simulation into CI pipelines and automated testing frameworks
Excellent communication skills in English

Experience hiring or technically leading engineers, or willingness to do so

Nice to have

Experience with GPU-based simulation and large-scale cloud simulation infrastructure (AWS, containerization, provisioning tools)
Familiarity with CI/CD systems (GitHub Actions, Jenkins, or similar)
Knowledge of reinforcement learning or imitation learning for robot policy training
Knowledge of closing sim-to-real gap
Strong software engineering skills in modern C++
Experience with ROS2

Gravis is an equal opportunity employer. We are committed to inclusion and diversity, and do not discriminate based upon race, colour, ancestry, national origin, religion, sex, sexual orientation, age, gender identity, gender expression, disability, veteran status, or other legally protected characteristics. All qualified people are highly encouraged to apply.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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