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Role overview

A Staff Machine Learning Engineer position focused on Bird's-Eye View (BEV) and multi-modal perception for autonomous trucking. The role centers on designing and advancing perception models that fuse heterogeneous sensor data into rich spatial representations of the driving environment. It is a technical leadership track concentrated on model innovation and maturity rather than downstream feature integration.

Responsibilities

• Define and execute the technical roadmap for BEV-based perception models spanning detection, segmentation, road topology, and scene understanding.
• Architect multi-modal networks that unify camera, LiDAR, radar, and HD map inputs into cohesive spatial representations.
• Build foundational perception models using BEV transformers, voxel-based encoders, or implicit scene representations.
• Own large-scale training pipelines including data sampling, augmentation, distributed training, and hyperparameter optimization.
• Improve model robustness and generalization across long-tail conditions such as low visibility, occlusions, and rare scene configurations.
• Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance.
• Collaborate with sensor calibration, mapping, and fusion teams to align perception model interfaces.
• Mentor ML engineers on experimentation, code quality, and model validation practices.
• Track relevant research, including self-supervised learning, large-scale pretraining, and foundation models for 3D perception.

Requirements

• 10+ years of experience in deep learning for perception, 3D vision, and/or autonomous systems.
• M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, or a related field, or equivalent practical experience.
• Demonstrated expertise in BEV modeling, 3D scene understanding, and multi-view fusion.
• Strong background in multi-modal sensor fusion, particularly camera and LiDAR integration.
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Experience with large-scale data pipelines, distributed training, and experiment management systems.
• Track record of leading ML model innovation and mentoring technical teams.

Nice to have

• Production experience in autonomous driving or robotics perception.
• Familiarity with MLOps tooling and infrastructure such as Ray.
• Hands-on expertise with BEV-based architectures, LiDAR-vision fusion, or spatial-temporal modeling.
• Understanding of 3D labeling, calibration, and sensor simulation pipelines.
• Publications or open-source contributions at top venues such as CVPR, ICCV, NeurIPS, ICRA, or CoRL.
• Awareness of deployment constraints including latency, memory, and accuracy tradeoffs.

Benefits and work setup

• Hybrid work available in Ann Arbor, MI, with remote options across the United States.
• Compensation range of $215,500–$258,600 USD, plus bonus and stock options.
• 100% employer-paid medical, dental, and vision premiums for full-time staff.
• 401(k) plan with a 6% employer match.
• Flexible scheduling, generous paid vacation available from the start date, and company-wide holiday closures.
• AD+D and life insurance coverage.