ML Engineer, II - Simulation Enablement
Job description
About the role
This role sits at the intersection of platform engineering and autonomy to scale Torc Sim adoption. The position translates simulation platform constraints into product improvements for perception and behavior models.
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
What you'll do
Data operations connect to the simulation environment, and persistent storage runs at scale to support replay and recompute.
Autonomy models execute replay and recompute workflows at scale, followed by metric evaluation, through an integrated data pipeline.
The visualization tooling is adopted and improved, with UI and UX feedback fed into the development backlog.
You become a domain expert on your partner model, such as Camera or Object Tracking, enabling fluent translation between simulation platform and autonomy model teams.
Workflows and onboarding documentation are created so adoption extends beyond direct hands-on support.
Requirements
A Bachelor's Degree in Computer Science, Robotics, Electrical Engineering or a related technical field plus 4+ years of experience, OR a Master's Degree plus 2+ years of experience.
Python skills are strong, with experience building or operating data pipelines at scale.
Experience working with simulation, replay, or model validation is present.
Familiarity with autonomy or robotics ML models for perception, tracking, prediction, or planning and their data is demonstrated.
Comfort working across cloud storage and compute is required.
Cross-team communication skills support daily collaboration with platform and embedded Autonomy teams.
A hands-on problem-solving bias drives work on broken data pipelines and explaining metric discrepancies to model owners.
Nice to have
Prior experience in a forward-deployed engineer, solutions engineer, or embedded platform role is valued.
Hands-on experience with Camera, Lidar, Vehicle Intent, or Object Tracking models is beneficial.
Experience with simulation or replay frameworks for autonomous vehicles or robotics is noted.
Familiarity with visualization tooling such as Foxglove, OpenGL, or Three.js is recognized.
Experience with large sensor data formats such as MCAP and Parquet, and associated processing tools such as PyArrow, Daft, or Pandas is helpful.
Experience with distributed compute/orchestration frameworks such as Ray, Anyscale, or AWS HyperPods is considered.
Infrastructure-as-code experience using Terraform and CI systems such as GitHub Actions is preferred.
Practical notes
Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.
Career growth
Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.