Note: The job is a remote job and is open to candidates in USA. Torc Robotics develops software for automated trucks and autonomous driving systems. The Staff Machine Learning Engineer will lead the development of BEV and multi-modal perception models that combine camera, LiDAR, radar, and HD map data for autonomous driving. The role focuses on model architecture, large-scale training, robustness, evaluation, technical leadership, and mentoring.
Responsibilities
- Lead BEV model development: define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology, and scene understanding)
- Design advanced multi-modal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations
- Develop foundational perception models leveraging BEV transformers, voxel-based encoders, or implicit scene representations
- Own large-scale training workflows — from data sampling strategies and augmentation pipelines to distributed training and hyperparameter optimization
- Advance model robustness and generalization, addressing 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 cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces
- Mentor and guide ML engineers, cultivating best practices in experimentation, code quality, and model validation
- Stay at the forefront of ML research, exploring self-supervised learning, large-scale pretraining, or foundation models for 3D perception
Skills
- 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 related field (or equivalent practical experience)
- Proven expertise in BEV modeling, 3D scene understanding, and multi-view fusion
- Strong background in multi-modal sensor fusion, particularly integrating camera and LiDAR data
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Experience with large-scale data pipelines, distributed training, and experiment management systems
- Demonstrated leadership in driving ML model innovation and mentoring technical teams
- Experience with autonomous driving or robotics perception in production environments
- Experience with MLOps and infrastructure tools (Ray)
- Hands-on expertise in BEV-based ML architectures, LiDAR-vision fusion, or spatial-temporal modeling
- Familiarity with 3D labeling, calibration, and sensor simulation pipelines
- Track record of publications or open-source contributions in top-tier venues (CVPR, ICCV, NeurIPS, ICRA, CoRL)
- Understanding of performance tradeoffs and deployment constraints (latency, memory, accuracy)
- For this position, we are open to hiring in Ann Arbor, MI in a hybrid capacity
- We are also open to hiring Remote in the United States
Benefits
- A competitive compensation package that includes a bonus component and stock options
- 100% paid medical, dental, and vision premiums for full-time employees
- 401K plan with a 6% employer match
- Flexibility in schedule
- Generous paid vacation (available immediately after start date)
- Company-wide holiday office closures
- AD+D and Life Insurance
- Hybrid work capacity in Ann Arbor, MI
- Remote work in the United States
- Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package
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