Senior Data Pipelines & ML Infra Engineer

Torc Robotics; Ann Arbor (MI); On-site; USD 116,000 - 193,000; Full time

Benefits offered by this job

Bonus and stock options

Full medical, dental, vision coverage

401K with employer match

Flexible schedule

Holiday office closures

AD+D and Life Insurance

Job summary

Torc Robotics is seeking an experienced software/ML infrastructure engineer to own and scale data tagging pipelines for autonomous trucking projects. You will write production Python, deploy on Databricks, and manage AWS infrastructure to support high-volume processing.

You will partner with ML teams to bring tagging and classification models into production, implement observability, and ensure data quality across petabyte-scale logs while prioritizing reliability and cost efficiency.

Qualifications

• BS or MS in Computer Science, Engineering, or a related field, with 5+ years of software engineering experience, including production data pipeline or ML infrastructure work.
• Strong Python skills, with experience building and maintaining production data or ML pipelines.
• Hands‑on CI/CD experience, GitHub Actions required.
• Required experience with Databricks for large scale data processing and orchestration.
• Required experience with AWS, including infrastructure‑as‑code (Terraform or CloudFormation) for provisioning distributed processing infrastructure.
• Experience processing large scale time series or unstructured datasets.
• Experience with observability tooling (e.g., Datadog, Grafana, CloudWatch) for production pipeline monitoring and alerting.
• Experience integrating and deploying ML models into production systems — serving, monitoring, and rollback, not just training.
• Strong communication skills to work across ML, perception, and simulation teams.

Responsibilities

• Integrate and deploy automated event-tagger into production pipelines, running and monitoring tagging tasks at scale across petabytes of vehicle log data.
• Build and maintain the data engineering pipelines that organize, structure, and catalog tagged scenario data into the observations database.
• Own CI/CD for the Auto Tagger pipeline using GitHub Actions, keeping deployments reliable, tested, and repeatable.
• Write production grade code in Python across the pipeline, from data ingestion and transformation through model integration and deployment.
• Build and operate on Databricks for large scale data processing, interactive querying and pipeline orchestration.
• Design, deploy, and scale AWS infrastructure (as code) to support high-volume, distributed processing of vehicle log pipelines — working with structured/tagged outputs and metadata.
• Instrument pipelines with logging, metrics, and alerting; own on-call response for tagging job failures and data quality regressions.
• Partner with ML engineers on the team to take tagging and classification models from development into a scalable, monitored production pipeline.
• Ensure data quality and metadata integrity as tagged events move from raw logs into the observations database used by perception, simulation, and systems teams.
• Troubleshoot and improve pipeline performance, reliability, and cost as data volume and model complexity grow.

Skills

Strong Python skills

CI/CD (GitHub Actions)

Databricks

AWS + IaC (Terraform/CloudFormation)

Observability tooling

ML model deployment

Data pipelines

Communication skills

Education

BS or MS in CS/Engineering

Tools

Terraform

CloudFormation

ROS bags

Parquet/Arrow

Job description

Torc Robotics is seeking an experienced software/ML infrastructure engineer to own and scale data tagging pipelines for autonomous trucking projects. You will write production Python, deploy on Databricks, and manage AWS infrastructure to support high-volume processing.

You will partner with ML teams to bring tagging and classification models into production, implement observability, and ensure data quality across petabyte-scale logs while prioritizing reliability and cost efficiency.