What to Expect
Our team manages multiple functions across Tesla that includes Devops, MLOps, Cloud Infrastructure (AWS, Azure, GCP), Factory SRE as well. 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.

What You’ll Do

• Mature our Machine Learning Operations Platform and advocate best practices to MLops engineers
• 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
• 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
• Package & deploy applications using Helm charts / deploy via ArgoCD
• Strong troubleshooting skills in Kubernetes workloads, networking, and CI/CD pipelines are required

• Participate in 24x7 on-call rotation

What You’ll Bring

• 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
• 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
• Degree in Computer Science, Computer Engineering, Electrical Engineering, Physics or proof of exceptional skills in related field or equivalent experience

Compensation and Benefits
Benefits

Along with competitive pay, as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire:

• Aetna PPO and HSA plans > 2 medical plan options with $0 payroll deduction
• Family-building, fertility, adoption and surrogacy benefits
• Dental (including orthodontic coverage) and vision plans, both have options with a $0 paycheck contribution
• Company Paid (Health Savings Account) HSA Contribution when enrolled in the High Deductible Aetna medical plan with HSA
• Healthcare and Dependent Care Flexible Spending Accounts (FSA)
• 401(k) with employer match, Employee Stock Purchase Plans, and other financial benefits
• Company paid Basic Life, AD&D, short-term and long-term disability insurance
• Employee Assistance Program
• Sick and Vacation time (Flex time for salary positions), and Paid Holidays
• Back-up childcare and parenting support resources
• Voluntary benefits to include: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
• Weight Loss and Tobacco Cessation Programs
• Tesla Babies program
• Commuter benefits
• Employee discounts and perks program

Expected Compensation
$133,440 - $292,800/annual salary + cash and stock awards + benefits
Pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
Tesla is an Equal Opportunity / Affirmative Action employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state or local laws.
Tesla is also committed to working with and providing reasonable accommodations to individuals with disabilities. Please let your recruiter know if you need an accommodation at any point during the interview process

• Mature our Machine Learning Operations Platform and advocate best practices to MLops engineers
• 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
• 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
• Package & deploy applications using Helm charts / deploy via ArgoCD
• Strong troubleshooting skills in Kubernetes workloads, networking, and CI/CD pipelines are required
• Participate in 24x7 on-call rotatio