ML Engineer, II - Simulation Enablement

Torc Sim is the simulation platform built by our Dataloop + Simulation division to help Autonomy teams replay, recompute, evaluate, and visualize their models against real and synthetic driving data at scale. We are hiring a Machine Learning Engineer to sit at the center of that growth: reporting to Dataloop + Simulation but living day-to-day with one or two Autonomy teams to drive Torc Sim adoption from the inside. You will be the person who makes sure a model team's replay/recompute jobs run at scale, that the metrics coming out are interpretable and auditable, and that engineers can visualize what their model is doing.

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

Requirements

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; A bias toward hands-on problem solving

Nice to have

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

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; Company-wide holiday office closures; AD+D and Life Insurance