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Scale's Physical AI business unit is dedicated to solving the data bottleneck across Robotics, Autonomous Vehicles, and Computer Vision. This position will be a key contributor in conducting applied research in Physical AI and developing ML pipelines for processing, training, and fine-tuning on data collected by Scale, with a specific focus on optimizing algorithms and pipelines to run efficiently on GPUs in the cloud. In this role, you will have the opportunity to advance research, shape Scale’s offerings, and expand the frontier of data and model evaluation for Physical AI. As an ML Systems Engineer on the Physical AI team, you will design and build platforms for scalable, reliable, and efficient serving of foundation models specifically tailored for physical agents. Our platform powers cutting-edge research and production systems, supporting both internal research discovery and external customer use cases for autonomous vehicles and robotics.
Job Responsibility:
Maintain fault-tolerant, high-performance systems for serving robotics-related models and foundation models at scale, ensuring low latency for real-time applications
Build an internal platform to empower model capability discovery, enabling faster iteration cycles for research teams working on robotics
Work closely with Robotics researchers and Computer Vision engineers to integrate and optimize models for production and research environments
Conduct architecture and design reviews to uphold best practices in system scalability, reliability, and security
Develop monitoring and observability solutions to ensure system health and real-time performance tracking of model inference
Own projects end-to-end, from requirements gathering to implementation, in a fast-paced, cross-functional environment
Requirements:
4+ years of experience building large-scale, high-performance backend systems, with deep experience in machine learning infrastructure
Deep experience optimizing computer vision and other machine learning algorithms for cloud environments, including GPU-level algorithm optimizations (e.g., CUDA, kernel tuning)
Strong skills in one or more systems-level languages (e.g., Python, Go, Rust, C++)
Deep understanding of serving and routing fundamentals (e.g., rate limiting, load balancing, compute budgets, concurrency) for data-intensive applications
Experience with containers (Docker), orchestration (Kubernetes), and cloud providers (AWS/GCP)
Familiarity with infrastructure as code (e.g., Terraform)
Proven ability to solve complex problems and work independently in fast-moving environments
Nice to have:
Exposure to Vision-Language-Action (VLA) models
Knowledge of high-performance video processing (e.g., FFmpeg, NVDEC/NVENC) or 3D data handling (point clouds)
Familiarity with robotics middleware (e.g., ROS/ROS2) or AV data formats
What we offer:
Comprehensive health, dental and vision coverage
Retirement benefits
A learning and development stipend
Generous PTO
Additional benefits such as a commuter stipend (may be eligible)