Staff MLOps Engineer - RL Infrastructure

JOB SUMMARY

We are seeking an experienced MLOps Engineer to own and maintain our cutting-edge reinforcement learning (RL) training infrastructure. In this role, you will be responsible for the entire lifecycle of our RL systems, from managing cloud resources to optimizing job submission and deployment. You will work closely with our AI researchers to ensure they have a stable, efficient, and scalable platform to develop and train next-generation RL models.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

• Design, Deploy, and Maintain Infrastructure: Manage and scale our RL training clusters on major cloud platforms (e.g., GCP, AWS, Azure) using infrastructure-as-code principles.
• Orchestration and Deployment: Utilize container orchestration tools (e.g., Kubernetes, Docker Swarm) to manage the deployment and scaling of our applications and clusters.
• Job Scheduling and Execution: Implement and manage tooling for submitting and monitoring large-scale distributed training jobs using modern distributed computing frameworks (e.g., Ray, Slurm).
• Database and Storage Management: Oversee our cloud-native database solutions, ensuring efficient storage and retrieval of large datasets, including images.
• Developer Tools: Create SDKs, documentation, and CLI/GUI tooling that make it easy for researchers to launch experiments, visualize results, and debug issues without infrastructure expertise.
• System Optimization: Implement robust monitoring, logging, and alerting to ensure the reliability, performance, health and of the training infrastructure.
• CI/CD and Automation: Develop and maintain CI/CD pipelines for automated testing, data processing, benchmarking, and model experimentation.
• Cross-functional Collaboration: Work closely with AI researchers and robotics engineers to understand pain points, optimize training workflows, and develop solutions that accelerate development cycles.

SKILLS AND REQUIREMENTS

• Strong software engineering fundamentals (testing, code review, documentation, git) and proven experience in a backend or infrastructure role.
• Professional experience managing cloud infrastructure on a major cloud platform (e.g., GCP, AWS, Azure).
• Hands-on experience with infrastructure-as-code tools (e.g., Terraform, Ansible, CloudFormation).
• Familiarity with ML frameworks (PyTorch, TensorFlow) and understanding of model training workflows.
• Proficiency with containerization and orchestration technologies (e.g., Kubernetes, Docker).
• Understanding of distributed computing concepts and cluster management for compute-intensive workloads.
• Solid understanding of Python and experience with scripting for automation and tooling.

Bonus Qualifications:

• Experience with job scheduling and distributed computing frameworks like Ray, Slurm, or LSF.
• Experience with hyperparameter tuning frameworks (e.g., Hydra) and their integration with robotics simulation platforms like IsaacLab.

• Experience managing and optimizing cloud-native databases (e.g., Google Cloud SQL, Amazon RDS, Spanner) for large-scale data.
• Experience in a high-performance computing (HPC) environment, especially with GPU-accelerated workloads.
• Experience with robotics simulation tools (IsaacSim, MuJoCo, Gazebo) or game engines.
• A strong understanding of networking and security principles within a cloud environment.

EDUCATION and/or EXPERIENCE

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
• Minimum of 4 years of professional, full-time experience building and maintaining reliable, scalable systems.
• Exposure to ML/data engineering infrastructure.
• Experience building tools or platforms used by other developers and researchers.

PHYSICAL REQUIREMENTS 

• Prolonged periods of sitting at a desk and working on a computer
• Must be able to lift 15 pounds at times
• Vision to read printed materials and a computer screen
• Hearing and speech to communicate