ML Engineer, II - Simulation Enablement

Summary

Torc Robotics is a leader in autonomous driving technology focused on developing software for automated trucks. They are seeking a Machine Learning Engineer to drive the adoption of their simulation platform, Torc Sim, by collaborating with autonomy teams to ensure smooth operation and integration of models.

Responsibilities

• Act as the embedded point of contact between Dataloop + Simulation and one to two Autonomy teams, driving hands-on adoption of Torc Sim
• Implement the end-to-end data flow: data ops platform → simulation environment → persistent storage running at scale
• Onboard Autonomy models to execute replay/recompute workflows at scale, as well as the subsequent metric evaluation
• Ensure adoption of the visualization tooling and bring back UI/UX improvements for our development backlog
• Become a domain expert on the model(s) your partner team owns (e.g., Camera, Lidar, Vehicle Intent, Object Tracking) so you can implement and own integrations
• Debug issues that span the full stack, from data ingestion, through simulation execution, to storage and metrics — and drive them to resolution
• Translate on-the-ground feedback from Autonomy engineers into concrete requirements for the Dataloop + Simulation product roadmap
• Document workflows and onboard new users so adoption scales beyond your own hands-on support

Skills

• Bachelor's Degree in Computer Science, Robotics, Electrical Engineering or a related technical field plus demonstrated competencies typically acquired through 4+ years of experience, OR Master's Degree plus 2+ years of experience
• Strong Python skills and experience building or operating data pipelines at scale
• Experience working with simulation, replay, or model validation
• Familiarity with autonomy or robotics ML models (perception, tracking, prediction, or planning) and the data they consume
• Comfort working across cloud storage and compute
• Strong cross-team communication skills — you'll be translating between a platform team and one or two embedded Autonomy teams on a daily basis
• A bias toward hands-on problem solving: you're as comfortable debugging a broken data pipeline as you are explaining a metric discrepancy to a model owner
• Prior experience in a forward-deployed engineer, solutions engineer, or embedded platform role
• Hands-on experience with Camera, Lidar, Vehicle Intent, or Object Tracking models specifically
• Experience with simulation or replay frameworks for autonomous vehicles or robotics
• Familiarity with visualization tooling such as Foxglove, OpenGL, or Three.js
• Experience with large sensor data formats (MCAP, Parquet) and associated processing tools (PyArrow, Daft, Pandas)
• Experience with distributed compute/orchestration frameworks (Ray, Anyscale, AWS HyperPods)
• Infrastructure-as-code experience (Terraform) and CI systems (GitHub Actions)

Qualifications

Must Haves

• Bachelor's Degree in Computer Science, Robotics, Electrical Engineering or a related technical field plus demonstrated competencies typically acquired through 4+ years of experience, OR Master's Degree plus 2+ years of experience
• Strong Python skills and experience building or operating data pipelines at scale
• Experience working with simulation, replay, or model validation
• Familiarity with autonomy or robotics ML models (perception, tracking, prediction, or planning) and the data they consume
• Comfort working across cloud storage and compute
• Strong cross-team communication skills — you'll be translating between a platform team and one or two embedded Autonomy teams on a daily basis
• A bias toward hands-on problem solving: you're as comfortable debugging a broken data pipeline as you are explaining a metric discrepancy to a model owner

Nice to Haves

• Prior experience in a forward-deployed engineer, solutions engineer, or embedded platform role
• Hands-on experience with Camera, Lidar, Vehicle Intent, or Object Tracking models specifically
• Experience with simulation or replay frameworks for autonomous vehicles or robotics
• Familiarity with visualization tooling such as Foxglove, OpenGL, or Three.js
• Experience with large sensor data formats (MCAP, Parquet) and associated processing tools (PyArrow, Daft, Pandas)
• Experience with distributed compute/orchestration frameworks (Ray, Anyscale, AWS HyperPods)
• Infrastructure-as-code experience (Terraform) and CI systems (GitHub Actions)

Benefits

• 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