System Validation Engineer, AI Hardware

Job description

from Tesla careers

The Tesla AI Hardware team stands at the forefront of artificial intelligence innovation, developing custom silicon and compute infrastructure that redefine what's possible in machine learning. Our team designs advanced AI accelerators and the datacenter systems that scale them—from Dojo, Tesla's purpose-built supercomputer, to the infrastructure that trains massive neural networks powering Full Self-Driving (FSD) and Optimus, Tesla's humanoid robot. Before any server, rack, or datacenter system goes into production, it must be rigorously validated to ensure it delivers the performance, reliability, and efficiency our AI workloads demand.

We are seeking a Validation Engineer to build comprehensive test frameworks and validation infrastructure for our AI datacenter systems. You will develop automated test suites, stress-test hardware under production workloads, and ensure every component—from individual servers to complete datacenter deployments—meets Tesla's exacting standards for performance and reliability.

Responsibilities

• Design and implement automated validation frameworks for AI datacenter systems—servers, racks, networking, and storage—to ensure production readiness
• Develop and execute test suites to validate hardware functionality, performance benchmarks, thermal characteristics, and long-term reliability under production AI workloads
• Build performance benchmarking and diagnostic tools to measure compute throughput, network bandwidth, storage I/O, power consumption, and thermal efficiency
• Automate data collection and analysis to track validation results, identify trends, and generate actionable insights for hardware design improvements
• Develop sw deployment, orchestration and testing pipelines with tools like Docker and configuration testing (e.g., Ansible)

Requirements

• Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or equivalent experience
• 3+ years of experience in system validation, hardware testing, or test automation
• Proficiency in Python for test automation and data analysis
• Proven ability to design and implement automated test frameworks, including burn-in testing, stress testing, and performance benchmarking
• Experience with datacenter systems validation and understanding of servers, networking equipment, storage systems, and power/thermal infrastructure
• Experience with distributed computing, containerization, orchestration, and CI/CD tools (e.g., SLURM, Docker, Kubernetes, Ansible)
• Strong debugging skills across hardware and software domains with ability to analyze complex data sets

Preferred Qualifications:

• Experience with AI/ML infrastructure or GPU cluster validation
• Background in statistical analysis, data visualization (Grafana, Kibana, Prometheus), or performance modeling
• Familiarity with Linux system administration, scripting (Bash, shell), and test frameworks (pytest, unittest)
• Experience utilizing lab test equipment and integrating it into automated hardware validation testing processes
• Knowledge of HPC technologies (Ethernet, InfiniBand, RDMA, PCIe, NVMe, or high-speed interconnects)

Compensation & Benefits