Torc Robotics · 5 days ago
Staff, ML Engineer - E2E
Ann Arbor, MI
Full-time
Hybrid
Lead/Staff
$220K/yr - $330K/yr
10+ years expTorc Robotics is a leader in autonomous driving technology, focused on developing software for automated trucks. The Staff Machine Learning Engineer will lead the design and deployment of learning-based architectures, driving advancements in closed-loop autonomous driving performance.
Autonomous VehiclesRoboticsSoftware
Responsibilities
Lead E2E model design and development — define architectures that directly map multi-modal sensor inputs (camera, LiDAR, radar, HD maps) to mid- or high-level driving actions or cost functions
Drive large-scale training and evaluation for E2E learning, integrating data from perception, behavior prediction, and control systems
Develop and refine learning objectives that align with real-world driving metrics: safety, comfort, compliance, and efficiency
Architect scalable pipelines for multi-task, multi-modal learning, leveraging both real-world and synthetic data
Prototype and evaluate new paradigms such as differentiable planning, imitation learning, reinforcement learning, and world models for AV behavior
Collaborate cross-functionally with Perception, Prediction, and Motion Planning teams to align interfaces and ensure consistency between learned and modular components
Establish robust evaluation frameworks for E2E performance, including closed-loop simulation and on-road validation
Mentor engineers and scientists in large-scale experimentation, model interpretability, and data-driven debugging
Stay at the frontier of ML research, exploring advancements in foundation models, sequence modeling, self-supervision, and generative world representations
Qualification
Required
10+ years of experience developing deep learning systems for perception, planning, or control
M.S. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or related field (or equivalent practical experience)
Deep expertise in multi-modal ML, sequence modeling, or policy learning (e.g., Transformers, diffusion models, imitation learning)
Proven track record in large-scale model training and optimization for real-world tasks
Strong proficiency in Python, PyTorch, or TensorFlow, and experience with distributed ML frameworks
Solid understanding of sensor fusion, spatiotemporal modeling, and vehicle dynamics
Demonstrated leadership in driving technical roadmaps, mentoring teams, and delivering production-quality ML solutions
Experience using Ray
Preferred
Experience developing E2E or mid-to-end models for autonomous driving, ADAS, or robotics
Familiarity with differentiable cost maps, latent space planning, or behavior cloning / reinforcement learning in driving domains
Hands-on experience with simulation-in-the-loop training and evaluation
Understanding of safety validation and interpretability for learned driving systems
Publications or open-source contributions in top-tier ML or robotics venues (CVPR, NeurIPS, ICLR, ICRA, CoRL)
Experience with foundation models or large-scale multimodal pretraining for perception and planning
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 and generous paid vacation (available immediately after start date)
Company-wide holiday office closures
AD+D and Life Insurance
Company
Torc Robotics
Torc provides L4 end-to-end self-driving software for mobility, trucking, mining, and defense markets through strategic partnerships
501-1000 employees
H1B Sponsorship
Torc Robotics has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (100)
2024 (41)
2023 (33)
2022 (40)
2021 (14)
2020 (7)
Funding
Current Stage
Late StageTotal Funding
unknown2019-03-29Acquired
Leadership Team
Peter Vaughan Schmidt
CEO
April Alexander
Human Resources Business Partner
Recent News
2025-12-02
2025-09-12
2025-09-09
Company data provided by crunchbase