Staff, ML Engineer - E2E at Torc Robotics

, , Canada -

Full Time

Start Date

Immediate

Expiry Date

24 Feb, 26

Salary

313800.0

Posted On

26 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Python, PyTorch, TensorFlow, Sensor Fusion, Vehicle Dynamics, Model Interpretability, Data-driven Debugging, Reinforcement Learning, Imitation Learning, Simulation-in-the-loop Training, Multi-modal Learning, Behavior Cloning, Foundation Models, Sequence Modeling

Industry

Truck Transportation

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. Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer. Meet the Team: As a Staff Machine Learning Engineer focused on End-to-End (E2E) Model Development, you will lead the design and deployment of learning-based architectures that connect perception inputs to driving decisions — advancing the frontier of closed-loop autonomous driving performance. You’ll architect and push forward Torc’s End – to – End approaches through unified, differentiable pipelines, leveraging massive real-world and simulated datasets to continuously improve system intelligence. This is a high-impact technical leadership role focused on core model research and large-scale ML development, not feature-layer logic or rule-based planning. What You’ll Do 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. What You’ll Need to Succeed M.S. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or related field (or equivalent practical experience). 10+ years of experience developing deep learning systems for perception, planning, or control. 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 Bonus Points! 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 Perks of Being a Full-time Torc’r (Canada) Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers: A competitive compensation package that includes a bonus component and stock options Medical, dental, and vision for full-time employees RRSP plan with a 4% employer match Public Transit Subsidy (Montreal area only) Flexibility in schedule and generous paid vacation Company-wide holiday office closures 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 labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. CAD Compensation Range: $209,300-313,800 CAD Job ID: 102406

Responsibilities

Lead the design and deployment of learning-based architectures for autonomous driving. Collaborate with cross-functional teams to ensure consistency between learned and modular components.