Scale AI

Software Engineer Argentina; Uruguay Software Engineer - Robotics & Autonomous Systems 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: - At least 6 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 </ul&g

Director of Engineering, Physical AI Role Overview The Director of Engineering will report to the General Manager of Physical AI, and will be responsible for leading a multi-disciplinary engineering organization. In this senior leadership role, you will own the execution of the Physical AI Data Engine — the platform powering the next generation of Physical AI/Embodied AI. You will collaborate closely with Operations and GTM to guide product direction and help solve the data bottleneck that stands between today's robotics research and real-world deployment. This role requires significant ownership in a fast-paced environment and you will motivate internal teams to set the pace for business growth. Travel will come into play. Key Responsibilities: - Set and drive the technical vision across data collection infrastructure, teleoperation systems, ML training pipelines, model evaluation frameworks, annotation tooling, and research - Lead a multidisciplinary engineering organization—spanning engineering managers, software engineers, ML engineers, and ML research scientists—while designing the organizational structure, talent strategy, and culture required to scale rapidly without compromising on quality or strategic alignment - Maintain exceptional technical and operational excellence by deeply understanding team deliverables, asking incisive questions, identifying slipping standards early, and knowing precisely when to step in - Drive cross-functional alignment across Engineering, Operations, and GTM on platform architecture, release processes, and shared priorities - Collaborate with researchers and clients to architect and deliver scalable, production-grade data infrastructure tailored for complex robotics workloads Required Qualifications: - Bachelor's degree in Engineering, Robotics, Computer Science, or a related technical field - 8+ years of engineering experience in fast-paced environments, including 4+ years direct people management demonstrated history of recruiting, mentoring, and developing high-performing technical teams through rapid growth and change - Experience leading technical execution for complex hardware-software systems, with deep domain knowledge in Robotics, Autonomous Vehicles, Computer Vision, and/or Machine Learning strongly preferred - Deep fluency in the ML development lifecycle — training pipelines, data flywheels, and evaluation frameworks as systems you've built and owned - Comfortable leading teams across Python, C++, and TypeScript/Node stacks, distributed systems, cloud infrastructure (AWS, Kubernetes), and workflow orchestration (Temporal, Airflow) - Proven ability to independently navigate, execute effectively amidst ambiguity, and strong attention to detail - Strong operator and communicator to create tight feedback loops between teams, surface problems early, and drive decisions with clarity across technical and executive audiences Nice to Have - MS or PhD is a plus, though strong practical experience is equally valued. - Hands-on experience with teleoperation systems (ALOHA, UMI, hand tracking), robotic hardware platforms, or imitation learning - Background in sensor fusion, SLAM, or 3D data processing - Experience scaling data collection systems globally <div class="content-pay-t