Senior, ML Engineer - ML Ops Framework at Torc Robotics

Montreal, Quebec, Canada -

Full Time

Start Date

Immediate

Expiry Date

24 May, 26

Salary

298800.0

Posted On

23 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

ML Ops, Cloud Tooling, Terraform, AWS Managed Services, EKS, Ray, Parquet Processing, PyArrow, Daft, Pandas, Vector Databases, LanceDB, MCAP/ROS, PyTorch, Lightning, Gymnasium

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: Our team is tasked with enabling ML developers across Torc to build the most advanced autonomous vehicle solutions at scale. We are the team responsible for enabling the full ML experimentation process, from ideation, to training and validation, to comparison between models on the same lineage to conversion of an optimal model to a viable truck format. We are an AWS shop, using Lightning & Ray for our distributed ML work, and we hope you are a Senior ML Engineer who can help us drive the future of autonomous freight. What You'll Do: You will work to build, maintain and improve our Torc ML frameworks including the following tools: Cloud tooling Terraform, AWS Managed Services, EKS, Ray Data ops Parquet processing (PyArrow, Daft, Pandas), vector databases / LanceDB, MCAP/ROS ML Development pipeline Python, Ray, PyTorch, Lightning, Gymnasium Logging & aggregation, Experiment tracking, Model registry & lineage Visualization & dashboarding (e.g., tableau, voxel51, rerun) Design organization wide systems and platforms for ML Ops. Be a key driver of the technical requirements and the adoption of the AV 3.0 Dev Kit with internal stakeholders. Drive velocity in development, review, iteration, and be one of the key contributors to help drive the team forward. You will be a trusted technical voice to drive collaboration of the Data Loop & Simulation organization within the AI Division at Torc. What You'll Need to Succeed: Bachelor’s degree in Computer Science, Software Engineering, or related field with 6+ years of professional experience. Master’s degree in Computer Science, Software Engineering, or related field with 3+ years of professional experience. Experience with large scale distributed training, specifically working with Ray. You are well versed in open-source software and make contributions. You have built ML solutions that have reached production. You feel comfortable with the data science and ML iteration/lifecycle. You can balance multiple stakeholders when designing components of the ML development lifecycle. Ideal: You are familiar with model optimizations/compression for edge deployment. You have familiarity with compute and data platforms. You feel comfortable with the data science and ML iteration/lifecycle. You can balance multiple stakeholders when designing components of the ML development lifecycle. Ideal: You are familiar with model optimizations/compression for edge deployment. You want to build, maintain, grow, and improve our ML platform. You know that a healthy software deployment requires mature software practices Ideal: You love to speak with users and help them reach their goals Experience in the autonomous vehicle (L4) or adjacent industry. Bonus Points! Reinforcement Learning for physical AI Publications in relevant conferences (NeurIPS, ICML, CVPR, ICLR, etc.) Contribute to open source software and speaking at seminars or conferences. Knowledge of English is required since the selected candidate will need to collaborate daily with English-speaking colleagues in the United States and work with technical documentation written exclusively in English. 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. Hiring Range for Job Opening Canada Pay Range $199,200 to $298,800 CAD Job ID: 102397

Responsibilities

The role involves building, maintaining, and improving Torc's ML frameworks, focusing on cloud tooling, data operations, and the ML development pipeline. This includes designing organization-wide ML Ops systems and driving technical requirements for internal stakeholders.