Senior ML Infra Engineer — Production Data Pipelines & AWS

Torc Robotics; United States; Remote; USD 161,000 - 193,000; Full time

Benefits offered by this job

Stock options

Bonus component

Health, dental, vision coverage

Job summary

Torc Robotics is seeking a senior software engineer focused on data pipelines and ML infrastructure for the Auto Tagger team. You will deploy tagging workflows, build scalable data pipelines, and manage CI/CD and cloud infrastructure to process petabytes of vehicle data.

You will work with ML engineers to move tagging models into production, monitor data quality, and optimize cost and performance while ensuring observability and reliability in a fast-paced autonomy environment.

Qualifications

• BS or MS in Computer Science, Engineering, or related field, with 5+ years of software engineering experience.
• Strong Python skills, with production data or ML pipelines experience.
• Hands-on CI/CD experience, GitHub Actions required.
• Experience with Databricks for large-scale data processing and orchestration.
• Experience with AWS and infrastructure-as-code (Terraform or CloudFormation).
• Experience with large-scale time series or unstructured datasets.

Responsibilities

• Integrate and deploy automated event-tagger into production pipelines at scale.
• Build and maintain data engineering pipelines that organize and catalog data.
• Own CI/CD for the Auto Tagger using GitHub Actions.
• Write production-grade Python code for data ingestion, transformation, and model deployment.
• Operate on Databricks for data processing, querying, and orchestration.
• Design and deploy AWS infrastructure to support distributed processing.

Skills

Python

Production data pipelines

ML infrastructure

CI/CD

Databricks

AWS

observability tooling

Education

BS or MS in CS/Engineering

Tools

Databricks

AWS (IaC)

Terraform/CloudFormation

GitHub Actions

Parquet/Arrow

Job description

Torc Robotics is seeking a senior software engineer focused on data pipelines and ML infrastructure for the Auto Tagger team. You will deploy tagging workflows, build scalable data pipelines, and manage CI/CD and cloud infrastructure to process petabytes of vehicle data.

You will work with ML engineers to move tagging models into production, monitor data quality, and optimize cost and performance while ensuring observability and reliability in a fast-paced autonomy environment.