Software Engineer, Robotics

Scale AI Scale AI · Data AI · Argentina · AVCV / Robotics EPD

Software Engineer focused on building production systems for robotics data collection, model training pipelines, and evaluation infrastructure within Scale AI's Robotics business unit. The role involves working across backend and frontend stacks to develop end-to-end solutions for robotics and autonomous vehicle datasets, and collaborating with ML engineers and researchers.

What you'd actually do

  1. Own and architect large-scale data processing pipelines for robotics and autonomous vehicle datasets
  2. Build ML training and fine-tuning pipelines using Scale's robotics data
  3. Work across backend (Python, [Node.js](http://node.js/), C++), and frontend (React, TypeScript) stacks to build end-to-end solutions
  4. Develop tools and real-time systems for robotics data collection, teleoperation, model evaluation, data curation, and data annotation
  5. Design comprehensive monitoring and evaluation frameworks for robotics models and data quality

Skills

Required

  • Python
  • TypeScript/Node.js
  • React
  • distributed systems
  • workflow orchestration
  • cloud infrastructure (AWS, Temporal, Kubernetes, Docker)
  • databases (MongoDB, PostgreSQL)
  • data processing at large scale
  • cross-functional teams
  • ML engineers
  • researchers
  • customers
  • communication skills
  • high autonomy

Nice to have

  • C++
  • robotics hardware platforms
  • time synchronization
  • computer vision
  • SLAM
  • motion planning
  • imitation learning
  • autonomous vehicle data
  • lidar technologies
  • 3D data processing
  • ML model deployment
  • serving frameworks
  • teleoperation systems
  • VR interfaces
  • workflow orchestration systems (Temporal, Airflow)
  • published research
  • open-source contributions

What the JD emphasized

  • production systems
  • real-time robotic systems
  • timing constraints
  • data integrity
  • high-proficiency software engineering experience
  • complex systems
  • hard technical problems
  • real-time systems
  • timing constraints

Other signals

  • production systems for robotics data collection
  • model training pipelines
  • evaluation infrastructure
  • embodied AI systems