Machine Learning Systems Engineer, Scale AI

• The ideal candidate combines strong ML fundamentals with deep expertise in backend system design
• Experience: 4+ years of experience building large-scale, high-performance backend systems, with deep experience in machine learning infrastructure
• Algorithm Optimization: Deep experience optimizing computer vision and other machine learning algorithms for cloud environments, including GPU-level algorithm optimizations (e.g., CUDA, kernel tuning)
• Programming: Strong skills in one or more systems-level languages (e.g., Python, Go, Rust, C++)
• Systems Fundamentals: Deep understanding of serving and routing fundamentals (e.g., rate limiting, load balancing, compute budgets, concurrency) for data-intensive applications
• Infrastructure: Experience with containers (Docker), orchestration (Kubernetes), and cloud providers (AWS/GCP)
• IaC: Familiarity with infrastructure as code (e.g., Terraform)
• Mindset: Proven ability to solve complex problems and work independently in fast-moving environments

Desirable

• 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
• You’ll work in a highly collaborative environment, bridging the gap between Physical AI research and production engineering to accelerate innovation across the company
• Build & Scale: Maintain fault-tolerant, high-performance systems for serving robotics-related models and foundation models at scale, ensuring low latency for real-time applications
• Platform Development: Build an internal platform to empower model capability discovery, enabling faster iteration cycles for research teams working on robotics
• Collaborate: Work closely with Robotics researchers and Computer Vision engineers to integrate and optimize models for production and research environments
• Design Excellence: Conduct architecture and design reviews to uphold best practices in system scalability, reliability, and security
• Observability: Develop monitoring and observability solutions to ensure system health and real-time performance tracking of model inference
• Lead: Own projects end-to-end, from requirements gathering to implementation, in a fast-paced, cross-functional environment