Senior Machine Learning Engineer (Robotics)

TL;DR

Senior Machine Learning Engineer (Robotics): Developing and scaling end-to-end learning-based systems that map multi-modal sensor inputs to driving behavior for automated trucks with an accent on unified learning pipelines and closed-loop environments. Focus on improving model performance at scale, implementing architectures like Transformers and VLA, and driving iteration through large-scale data analysis.

Location: Remote in the United States or Hybrid in Ann Arbor, MI

Salary: $226,400 – $271,700 USD

Company

Developing software for automated trucks as part of the Daimler family to transform how the world moves freight.

What you will do

• Own the development and delivery of End-to-End ML models mapping multi-modal sensor inputs (camera, LiDAR, radar, maps) to driving outputs.
• Train and evaluate models using large-scale datasets from fleet logs, simulation, and synthetic data.
• Analyze model performance and identify failure modes to drive data-driven improvements in robustness.
• Design and refine training pipelines, data workflows, and evaluation strategies to increase iteration speed.
• Implement model architectures including imitation learning, reinforcement learning, transformers, and vision-language-action (VLA) models.
• Collaborate with Perception, Prediction, Planning, and Simulation teams to ensure autonomy stack alignment.

Requirements

• 6+ years experience (BS), 4+ years (MS), or 0-2 years (PhD) in ML, Robotics, or Computer Science.
• Track record of publications in top-tier conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, or CoRL.
• Strong programming skills in Python and PyTorch for producing production-quality ML code.
• Solid understanding of Transformers, BEV models, VLA/VLM approaches, or diffusion models.
• Experience developing and deploying ML models for autonomous systems or complex robotics environments.
• Must be based in the United States.

Nice to have

• Experience developing End-to-End or mid-to-end models for autonomous driving.
• Familiarity with closed-loop simulation and evaluation frameworks.
• Experience with reinforcement learning or imitation learning in real-world systems.
• Experience with distributed training frameworks like Ray.
• Understanding of vehicle dynamics, motion planning, or multi-agent systems.

Culture & Benefits

• Competitive compensation package including bonus components and stock options.
• 100% paid medical, dental, and vision premiums for full-time employees.
• 401K plan with a 6% employer match.
• Flexible schedule and generous paid vacation available immediately.
• Company-wide holiday office closures.