Sr. Site Reliability Engineer, Machine Learning Operations, Infrastructure

About the position

Our team manages multiple functions across Tesla that includes Devops, MLOps, Cloud Infrastructure (AWS, Azure, GCP), and factory site reliability. Continued development and automation of deployment, monitoring, self-healing and alerting processes is imperative to the success of our engineering groups. As a Site Reliability Engineer, you will be responsible for maintaining and improving our platform to ensure our cross functional teams have the necessary tools and resources to be productive.

Responsibilities

• Mature our Machine Learning Operations Platform and advocate best practices to MLops engineers and design and implement scalable, automated workflows for the complete ML lifecycle
• Maintain Kubernetes-based infrastructure for model training, deployment, and monitoring
• Develop solutions for workload orchestration and time-slicing using tools like Flyte and Ray
• Implement and optimize CI/CD pipelines tailored for machine learning applications
• Leverage GPU capabilities, including MIG, to maximize efficiency for AI/ML workloads
• Set up model monitoring systems to track performance, ensure robustness, and scale workloads as needed
• Collaborate with engineers to build and maintain robust, pipelines for training and inference workflows
• Develop Infrastructure-as-Code (IaC) solutions for deploying and managing cloud/on-prem ML environments
• Design and develop intuitive, user-friendly self-service portals using React to enable data scientists and engineers to manage ML pipelines, monitor models, and access resources seamlessly
• Participate in 24x7 on-call rotation

Requirements

• Strong hands-on experience with tools and frameworks like Kubernetes, Kubeflow, MLflow, Flyte, Ray
• Proven experience with React for building interactive web applications, especially self-service portals that enhance the user experience for managing ML pipelines and workflows
• Expertise in MIG, time-slicing, and scaling AI workloads efficiently
• Proficiency in Python, Golang and bash for pipeline development, and automation
• Model Deployment and Serving: Tensorflow Serving, TorchServe, FastAPI, Flask, REST/gRPC on scalable architectures
• Proficiency with Linux fundamentals and performance optimizations
• Experience with configuration management software (Ansible, etc.), systems monitoring & alerting (Prometheus, Grafana, Telegraf, Splunk, etc.)
• Strong analytical and problem-solving abilities to troubleshoot and optimize AI/ML systems
• Ability to collaborate with cross-functional teams, including data scientists, data engineers, and DevOps engineers, to deliver high-quality solutions
• Excellent troubleshooting skills in production
• Bachelor's Degree in Computer Science, Computer Engineering, Electrical Engineering, Physics or proof of exceptional skills in related field or equivalent experience