Combine rule-based logic with learned components (classifiers, embeddings, Vision-Language Models) where heuristics hit their ceiling, and make the call on which approach fits each scenario.
Build evaluation for the taggers you write: ground-truth sets, per-scenario precision and recall, failure analysis, and regression tests that catch quality drift.
Turn ambiguous stakeholder requests (“find interesting merges”) from perception, simulation, and systems teams into precise, testable scenario definitions mapped to Pegasus layers.
Own your taggers end to end into production: Python on Databricks/Spark, CI via GitHub Actions, and monitoring of both tag quality and job health.
Optimize taggers for scale and cost across petabytes of logs, favoring vectorized, distributed implementations over per-frame processing.
What You'll Need to Succeed
Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 6+ years of experience OR Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 3+ years of experience.
Demonstrated algorithm design on real-world sensor, time-series, or geospatial data, in areas such as perception, tracking, state estimation, prediction, planning, signal processing, or applied ML.
Strong Python skills, including NumPy, pandas, and SciPy, plus working proficiency in SQL.
Experience defining metrics and evaluating algorithm quality against labeled or ground-truth data.
Hands-on exposure to production data systems: distributed compute (Spark, Ray, or Databricks), a cloud platform (AWS preferred), and CI/CD.
Strong communication skills to work across ML, perception, and simulation teams.
Bonus Points!
Autonomous vehicle or robotics data experience: ROS bags/MCAP, HD maps, Frenet or lane-relative coordinates.
Familiarity with scenario description standards such as Pegasus layers or OpenSCENARIO.
Experience with VLMs, zero-shot classification, auto-labeling pipelines, or embedding/vector search for scenario extraction.
Experience with GitHub Actions, infrastructure-as-code (Terraform or CloudFormation), and observability tooling (Datadog, Grafana, CloudWatch).
Experience with model serving frameworks such as vLLM or SGLang.
Familiarity with columnar storage formats (Parquet, Arrow).
Perks of Being a Torc'r
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
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
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.
Job ID: R-102834
Hiring Range for Job Opening
US Pay Range
$160,800—$193,000 USD
