Senior Staff Engineer (ML Ops)

Join our team as a Senior Staff Engineer (ML Ops) and help define and build a cutting-edge AI platform. You will work closely with ML researchers, platform engineers, and autonomy teams to deliver an exceptional developer experience for training and deploying modern AI models.

Responsibilities

• Your role will involve leading the design and implementation of the AI Factory Reference Architecture, enabling AI research workflows, and building the infrastructure required for distributed training and simulation.
• You will also optimize shared GPU infrastructure, manage the data and model lifecycle, and develop deployment solutions.
• This is an opportunity to work at the intersection of modern AI, distributed systems, Kubernetes, and defense, making a direct impact on the future of autonomy systems.
• Lead the design and implementation of the AI Factory Reference Architecture, delivering a Kubernetes-native platform for AI development, distributed training, simulation, evaluation, and deployment.
• Partner directly with ML researchers to understand evolving training workflows and ensure the platform supports state-of-the-art AI frameworks, foundation model development, reinforcement learning, distributed training, and emerging research workflows.
• Build the infrastructure required to support distributed training, simulation, inference, and reinforcement learning workloads. Evaluate and integrate orchestration, scheduling, and resource management technologies to maximize scalability and developer productivity.
• Experience collaborating closely with ML researchers to translate research workflows into scalable platform capabilities
• Experience packaging and deploying cloud-native infrastructure using Terraform and Helm
• Experience with GPU scheduling concepts and large-scale AI workloads
• Deep understanding of modern AI training frameworks, including PyTorch, Hugging Face Transformers and distributed training techniques

Requirements

• Strong software engineering skills in Python and Golang and modern cloud-native technologies
• Experience operating GPU-accelerated infrastructure and distributed training systems
• Strong understanding of Kubernetes, Linux, networking, security, storage, and distributed systems
• Experience building Kubernetes-native AI or MLOps platforms supporting distributed machine learning workloads
• Experience contributing to or maintaining open-source infrastructure projects
• Experience supporting reinforcement learning, simulation-driven training, robotics, or autonomy workloads
• Experience with KAI, Slurm or other GPU scheduling technologies

Preferred

• Experience designing infrastructure for classified, sovereign, or air-gapped environments
• Experience with observability technologies such as OpenTelemetry, Prometheus, and Grafana
• Experience deploying and optimizing AI models for edge hardware
• Experience with Ray or other distributed AI orchestration frameworks

Pay

USD 280,000 - 420,000