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.
The Role
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.
The ideal candidate combines strong ML fundamentals with deep expertise in backend system design. You’ll work in a highly collaborative environment, bridging the gap between Physical AI research and production engineering to accelerate innovation across the company.
You Will:
- 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.
Ideally, You’d Have:
- 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.
Nice to Haves:
- 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.
Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
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 employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at [email protected]. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
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What We Do
Scale accelerates the development of AI applications by helping machine learning teams generate high-quality ground truth data. Our advanced LiDAR, image, video and NLP annotation APIs allow machine learning teams at companies like OpenAI, Lyft, Pinterest, and Airbnb focus on building differentiated models vs. labeling data.
