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

torcrobotics; United States; Hybrid; USD 180,000 - 260,000; Full time

Benefits offered by this job

Bonus + stock options

Medical, dental, vision covered

401K with 6% match

Flexible schedule & vacation

Company holidays

Job summary

Torc is seeking a Staff Machine Learning Engineer focused on BEV and multi-modal perception to lead model innovation for autonomous trucking perception. You will guide architecture, large-scale training, and data-driven improvements within Torc's perception stack.

Required are 10+ years in deep learning for perception and a strong background in BEV, 3D vision, and sensor fusion, with leadership experience and proficiency in Python and DL frameworks. Hybrid work options available in the US.

Qualifications

• 10+ years of experience in deep learning for perception, 3D vision, and autonomous systems.
• MS or PhD 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, especially camera and LiDAR.
• Proficiency in Python and DL frameworks (PyTorch, TensorFlow).
• Experience with large-scale data pipelines, distributed training, and experiment management systems.
• Demonstrated leadership in ML model innovation and mentoring teams.

Responsibilities

• Lead BEV model development and set the technical roadmap for BEV-based perception models.
• Design multi-modal architectures that fuse camera, LiDAR, radar, and HD maps into unified representations.
• Develop foundation perception models using BEV transformers, voxel encoders, or implicit scene representations.
• Own large-scale training workflows from data sampling to distributed training and hyperparameter optimization.
• Improve robustness and generalization for challenging conditions and diverse environments.
• Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer.
• Collaborate with sensors calibration, mapping, and fusion teams to ensure cohesive interfaces.
• Mentor ML engineers and promote best practices in experimentation and validation.
• Stay at the forefront of ML research with self-supervised learning and foundation models.

Skills

Deep learning

BEV modeling

3D vision

Sensor fusion

MLOps

Python

PyTorch

TensorFlow

Leadership

Education

MS/PhD in CS/EE/Robotics or equivalent

Tools

PyTorch

TensorFlow

Ray

Python

Job description

About the Company

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.

A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight.

Meet the Team

As a Staff Machine Learning Engineer specializing in BEV (Bird's-Eye View) and Multi-Modal Perception, you will lead the development of next-generation models that unify information across cameras, LiDAR and radar to deliver a rich spatial understanding of the driving environment. You will drive architectural innovation, large-scale model training, and data-driven improvements that directly advance the perception capabilities at the heart of Torc's autonomous driving stack. This is a technical leadership role focused on model innovation and maturity, not downstream feature integration.

What You'll Do

• 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.

What You'll Need to Succeed

• 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.

Bonus Points

• 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).

Work Location

Work Location: 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.

Perks of Being a Full-time Torc'r

• 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 matchFlexibility in schedule and generous paid vacation (available immediately after start date)Company-wide holiday office closures
• AD+D and Life Insurance

At Torc, we're committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc'rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities.

Even if you don't meet 100% of the qualifications listed for this opportunity, we encourage you to apply.

Our compensation reflects the cost of la