Software Engineer, Robotics
Scale AIMexico City, MXPosted 7 April 2026
Tech Stack
Job Description
Scale's Robotics business unit is dedicated to solving the data bottleneck in Physical AI across Robotics, Autonomous Vehicles, and Computer Vision. In this role, you'll be a key contributor building production systems for robotics data collection, model training pipelines, and evaluation infrastructure. You'll have the opportunity to own critical parts of our robotics platform, work directly with cutting-edge robotics and AV customers, and shape the future of embodied AI systems.
You Will:
Own and architect large-scale data processing pipelines for robotics and autonomous vehicle datasets
Build ML training and fine-tuning pipelines using Scale's robotics data
Work across backend (Python, Node.js, C++), and frontend (React, TypeScript) stacks to build end-to-end solutions
Develop tools and real-time systems for robotics data collection, teleoperation, model evaluation, data curation, and data annotation
Interact directly with robotics and AV stakeholders to understand their technical needs and drive product development
Design comprehensive monitoring and evaluation frameworks for robotics models and data quality
Solving complex, late-stage industry challenges in concurrent and real-time robotic systems, with strict attention to timing constraints and data integrity. This often involves deep investigation, reviewing academic papers, and direct collaboration with robotics vendors
Collaborate with ML engineers and researchers to bring robotics research into production
Deliver features at high velocity while maintaining system reliability and performance
Ideally, You Have:
3+ years of high-proficiency software engineering experience, with a strong background in complex systems and the ability to independently research, analyze, and unblock hard technical problems.
Strong programming skills in Python and TypeScript/Node.js for production systems
Experience with React and modern frontend development for 3D interfaces
Concurrent and real-time systems, with special attention to timing constraints
Understanding of distributed systems, workflow orchestration, and cloud infrastructure (AWS, Temporal, Kubernetes, Docker)
Experience with databases (MongoDB, PostgreSQL) and data processing at large scale
Track record of working with cross-functional teams including ML engineers, researchers, and customers
Strong communication skills and ability to operate with high autonomy
Nice to Haves:
Experience with C++
Experience with robotics hardware platforms (robotic arms, mobile robots, perception systems) with a focus on time synchronization
Background in computer vision, SLAM, motion planning, or imitation learning
Familiarity with autonomous vehicle data, lidar technologies, or 3D data processing
Experience with ML model deployment and serving frameworks
Knowledge of teleoperation systems (ALOHA, UMI, hand tracking) or VR interfaces
Experience with workflow orchestration systems (Temporal, Airflow)
Published research or open-source contributions in robotics or autonomous systems
PLEASE NOTE:
Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
About Us:
At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employm ... (truncated, view full listing at source)
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