10 months ago
Buenos Aires, ArgentinaSenior
Responsibilities
- Own and architect large-scale data processing pipelines for robotics and autonomous vehicle datasets.
- Build ML training and fine-tuning pipelines using robotics data.
- Develop end-to-end backend and frontend systems using Python, Node.js, C++, React, and TypeScript.
- Build tools for robotics data collection, teleoperation, model evaluation, and data-quality monitoring.
- Develop real-time systems for robotic control, sensor fusion, and perception pipelines.
- Design monitoring and evaluation frameworks for robotics models and data quality.
- Work directly with robotics and autonomous vehicle stakeholders and collaborate with ML engineers and researchers to bring robotics research into production.
- Deliver reliable, high-performance features at high velocity.
Requirements
- At least 6 years of software engineering experience, including 3 or more years in robotics, autonomous vehicles, or related fields.
- Strong production programming skills in Python and TypeScript/Node.js, with experience using React for modern frontend and 3D interfaces.
- Practical experience with robotics frameworks such as ROS or ROS2, simulation environments, or autonomous vehicle systems.
- Experience with distributed systems, workflow orchestration, cloud infrastructure, databases, and large-scale data processing.
- Experience collaborating with ML engineers, researchers, customers, and cross-functional stakeholders.
- Experience with C++, robotics hardware, computer vision, SLAM, motion planning, imitation learning, lidar, 3D data processing, ML model serving, or teleoperation is desirable.
- Published research or open-source contributions in robotics or autonomous systems are desirable.
Tech Stack
Categories
About Scale AI
Scale AI builds data annotation services and AI development tools for enterprises and government agencies, sold as a platform and managed services. Its products include the Scale Generative AI Platform for building and evaluating agents and the Data Engine for collecting, curating, and labeling training data, including RLHF and model evaluation. Founded in 2016 and headquartered in San Francisco, the company is privately held and works across domains from computer vision to LLM applications.