Staff, ML Engineer - Learned Behaviors at Torc Robotics

, , United States -

Full Time

Start Date

Immediate

Expiry Date

24 Feb, 26

Salary

329600.0

Posted On

26 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Reinforcement Learning, Behavior Cloning, Multi-Agent Interaction, Python, Pytorch, Data Strategies, Model Training Pipelines, Performance Metrics, Simulation, Mentoring, Behavior Prediction Networks, Distributed Computing, Large-Scale Data Pipelines, Model Deployment, Vehicle Dynamics Models

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 Learned Behaviors Machine Learning Engineer, you will lead the development, deployment, and scaling of Reinforcement Learning - driven behavior models for autonomous trucks. You will collaborate across perception, prediction, planning, and safety teams to craft learned behavior modules that generalize across scenarios and drive safe, efficient, and human-like decision making in real-world operations. This is a technical leadership role focused on model innovation and maturity, not downstream feature integration. What You’ll Do Lead architecture, development, and validation of learned behavior models (e.g., driver mimicry, multi-agent interaction, behavior cloning, reinforcement learning) for highway and freight-truck autonomy. Define and implement data strategies: collect, label, and curate large behavior datasets (in-vehicle, simulation, fleet logs) for training and evaluation. Develop scalable model training pipelines, infrastructure, and tooling to iterate quickly on behavior models, from prototype to production deployment. Design and track performance metrics, analyze model behavior, diagnose failure modes, and drive continuous improvement of learned behaviors in simulation and on-vehicle. Work with simulation, scenario, and validation teams to embed learned behavior models into verification and validation frameworks, ensuring coverage across operational design domains (ODDs). Mentor and lead mid and senior-level ML engineers, set technical direction, and drive best practices in learned behavior engineering across the team. What You’ll Need to Succeed 8+ years of professional experience (or equivalent) in applied machine learning in autonomous vehicles, robotics, simulation or a related domain. Advanced degree (MS or PhD preferred) in Machine Learning, Robotics, Computer Science, or related engineering field. Proven track record of designing and shipping learned behavior or policy models (e.g., behavior cloning, imitation learning, RL, multi-agent models) in production or near-production systems. Strong programming skills in the AI domain(Python, Pytorch (Lightning), pandas ) Experience with open-source distributed computing framework, specifically Ray. Deep understanding of ML architectures (e.g., RNNs, transformers, graph neural networks, behavior prediction networks) and system-level integration into autonomy stacks. Experience with large-scale data pipelines, model training infrastructure, versioning, and deployment (cloud and/or embedded). Bonus Points! Experience in trucking, highway autonomy, or heavy-vehicle behavior modeling. Expertise in Ray Experience integrating learned behavior modules into simulation and V&V workflows for autonomous systems. Familiarity with simulation frameworks, scenario libraries, and coverage of ODDs. Background in reinforcement learning in real-world or simulation, imitation learning of human drivers, or multi-agent modeling in vehicle contexts. Experience with sensor models, vehicle dynamics models, and domain adaptation from simulation to real world. Perks of Being a Full-time Torc’r 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 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 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. US Pay Range: $219,700.00 - $329,600.00 Job ID: 102403

Responsibilities

Lead the development and scaling of reinforcement learning-driven behavior models for autonomous trucks. Collaborate with various teams to ensure safe and efficient decision-making in real-world operations.