Torc Robotics
Senior, ML Engineer - ML Ops Framework
Remote - US, Ann Arbor, MI · Senior · Full-time
Sponsorship not specified$199k-$299kDetected 5 days ago
PythonVector DatabasesAWSTerraformMachine LearningPyTorchPandasData ScienceData VisualizationMLOpsA/B TestingCollaboration
About the role
- Even if you don't meet 100% of the qualifications listed for this opportunity, we encourage you to apply.
- Hiring Range for Job Opening US Pay Range $199,200 - $298,800 Job ID: 102397
Responsibilities
- You will work to build, maintain and improve our Torc ML frameworks including the following tools:
- Design organization wide systems and platforms for ML Ops.
- 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.
- You understand how to build ML systems that scale.
- You want to build, maintain, grow, and improve our ML platform
- 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.
Requirements
- 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 can balance multiple stakeholders when designing components of the ML development lifecycle.
- You have familiarity with compute and data platforms
- You have experience making tradeoffs for speed of development.
- You know that a healthy software deployment requires mature software practices
- Ideal: You are familiar with model optimizations/compression for edge deployment.
Skills
- 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)
- Be a key driver of the technical requirements and the adoption of the AV 3.0 Dev Kit with internal stakeholders.
- Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience.
Compensation
- Hiring Range for Job Opening US Pay Range $199,200 - $298,800 Job ID: 102397
Benefits
- 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.
- A competitive compensation package that includes a bonus component and stock options
- 100% paid medical, dental, and vision premiums for full-time employees
- Flexibility in schedule and generous paid vacation (available immediately after start date)
- AD+D and Life Insurance
Company info
- At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.
- 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.
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This listing is sourced directly from Torc Robotics's careers page and normalized into a canonical job model.