Join General Motors’ world-class development center in Munich, where we design advanced high-resolution imaging radar sensors that power Personal Autonomous Vehicles (PAVs).

Our distinctive approach combines sensor innovation with direct vehicle integration, enabling close collaboration with global teams such as Perception. This integrated model delivers a seamless user experience while upholding the highest standards of safety.

The radar systems developed in Munich play a critical role in our mission to harness self-driving technology to save lives, transform urban mobility, redefine travel time, and expand freedom of movement.

If you’re driven to tackle complex engineering challenges, see your work deployed in real-world autonomous vehicles, and contribute to meaningful change on a global scale, we invite you to join us.

About the role:

As a Staff Machine Learning Engineer, you will be part of the Munich Radar ML team, developing AI technology that converts radar data into actionable information for GM’s autonomous vehicles. You will collaborate closely with key stakeholders in Munich and USA to define technical strategy, train, evaluate, and maintain radar ML models. Furthermore, you will develop critical radar-specific tooling for preprocessing and feature extraction, while partnering with Munich based software and algorithm teams to ensure robust, resource-aware deployment. Your work is central to closing the loop between data, model development, and on-road performance, directly driving the Radar perception roadmap and safety targets.

Areas of Work:

End-2-End Optimization

: Work with upstream Radar Engineers in Munich as well as downstream AI Engineers in Munich and the US to align Radar Signal Processing and AI data consumption for a perfect mutual fit.
Data Pipeline:

Provide and maintain tools to the foundational pipeline that feeds our model training and evaluation.
Validation:

Provide AI driven validation frameworks for Radar data products
Synthetic Data Generation:

Contribute to the creation of realistic radar datasets to enhance their impact on training quality and accuracy.

What We're Looking For:

Analytical Thinking:

Ability to identify, structure, and efficiently solve complex problems.
ML Expertise:

Deep understanding of typical ML architectures and the corresponding data flywheel for AV Applications and how they get trained and optimized.
Radar Expertise:

Experience on modern radar signal processing and the data products generated by imaging radar systems. Deep understanding of the information content encoded in radar data.
Quick Learner:

Competence in quickly acquiring necessary knowledge for new challenges and adapting to emerging topics.
Flexibility:

Willingness to adapt to changing requirements and continuously embrace new technologies.
Cross Location Collaboration:

Willingness to align regularly with the GM-US team on technical progress and achievements.
Solid Foundation:

Strong foundational knowledge in relevant areas, enabling further specialization and deeper understanding.
Communication:

Ability to clearly communicate complex concepts and effectively align across teams.
Team Work:

Collaborative mindset with the ability to work effectively within interdisciplinary project teams.
Project Experience:

Proven track record of working in project teams towards a common goal.

Your Skills & Abilities

Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a related field
Strong experience in radar signal processing and data products generated by radar systems.
Strong experience building and scaling AI/ML systems for perception, autonomy, robotics, or related real-world systems
Deep hands-on experience with modern deep learning frameworks such as PyTorch and strong proficiency in Python
Experience working with model training pipelines, large-scale data workflows, and infrastructure enabling efficient model iteration across teams
Strong data processing skills using tools such as NumPy, Pandas, and Apache Spark
Strong experience with model validation, debugging, performance optimization, and error analysis under real-world constraints and timelines
Strong experience with multi-modal sensor fusion and perception pipelines using cameras, radar, lidar, or related sensing modalities
Experience defining metrics and evaluation methodologies for perception or autonomy systems
Strong communication skills enabling effective collaboration across engineering teams
Experience deploying or preparing ML models for production environments and understanding end-to-end deployment workflows.

This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location on a minimum of 3 days per week.

About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

Why Join Us

We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.

Non-Discrimination and Equal Employment Opportunities

General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.

We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire

Staff Machine Learning Engineer (Radar & AI)

Visa & sponsorship

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