Staff, Machine Learning Engineer - BEV/Multi-Modal Perception

torcrobotics; Deutschland; Hybrid; EUR 186.000 - 223.000; Vollzeit

Benefits dieser Stelle

Hybrid work in Ann Arbor

Remote options in US

Bonus and stock options

401(k) match

Paid vacation

Insurance coverage

Zusammenfassung

torcrobotics is seeking a Staff Machine Learning Engineer to lead BEV-based perception and multi-modal fusion for autonomous trucking. You will design perception models that fuse camera, LiDAR, radar, and HD map data to create rich spatial representations of the driving environment.

The role emphasizes technical leadership, model innovation, and mentorship, with a hybrid work setup in the US and remote options. Extensive experience in 3D perception and multi-modal fusion is required.

Qualifikationen

• 10+ years of experience in deep learning for perception, 3D vision, or autonomous systems.
• M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, or equivalent practical experience.
• 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 like PyTorch or TensorFlow.
• Experience with large-scale data pipelines, distributed training, and experiment management.
• Track record of leading ML model innovation and mentoring technical teams.

Aufgaben

• Define and execute the BEV-based perception roadmap for detection, segmentation, road topology, and scene understanding.
• Architect multi-modal networks that fuse camera, LiDAR, radar, and HD map inputs into cohesive spatial representations.
• Build foundational perception models using BEV transformers or voxel-based encoders.
• Own large-scale training pipelines including data sampling, augmentation, distributed training, and hyperparameter optimization.
• Improve model robustness across 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 interfaces.
• Mentor ML engineers on experimentation, code quality, and model validation practices.
• Track research in self-supervised learning, large-scale pretraining, and foundation models for 3D perception.

Jobbeschreibung

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.