Drive hands-on evaluation and improvement of AI and agentic systems for software engineering workflows.

Evaluate emerging and established AI tools, agent harnesses, and models on representative NVIDIA engineering workflows
Compare outcomes across task success, output quality, reliability, latency, cost, and enterprise readiness
Identify where AI is the best tool; uncover gaps and recommend AI-first approaches based on hands-on evaluation of modern AI-native tools
Design, implement, and validate reference workflows for coding, testing, code review, and troubleshooting using tools such as Codex, Claude, Cursor, Greptile, and CodeRabbit
Introduce technologies enabling massively parallel systems to improve turnaround time by an order of magnitude
Optimize performance and cost by finding bottlenecks across training, evaluation, and testing; improve throughput, latency, and efficiency
Partner with AI product vendors to understand the AI industry; share insights with internal leaders and developers
Work with core and emerging tool vendors to evaluate new capabilities, influence product roadmaps, resolve adoption issues, and integrate useful features into NVIDIA’s AI ecosystem
Provide AI tool and model selection guidance, recommended configurations, workflow examples, onboarding materials, and hands-on training

MS in EE/CS or equivalent experience
12+ years of work experience
Strong understanding of LLMs, machine learning, and agentic AI, including how models, agent harnesses, context, and tool use affect end-to-end task outcomes
Hands-on experience evaluating AI tools or models and applying LLMs to software engineering workflows, including experiment design, benchmark development, reproducibility, and failure analysis
Strong software engineering skills in Python, with experience in Java or Go, plus extensive scripting and automation experience
Experience building evaluation pipelines, developer tooling, or full-stack applications, including API integration, data management, and deployment in cloud or enterprise environments
Experience with CI/CD tooling such as Jenkins, Gitlab CI, Packer, Terraform, Artifactory, Ansible, Chef, or similar tools
Good understanding of distributed systems, including microservice architecture and REST APIs
Familiarity with software development, testing, code review, and build workflows; ability to translate evaluation results into clear technical recommendations and practical user guidance
Ability to work effectively across organizational boundaries to improve alignment and productivity between teams

Industry thought leader in AI with influence across the AI ecosystem to deliver forward-looking solutions
Expertise in agent harnesses, tool calling, MCP, retrieval-augmented generation (RAG), context management, or fine-tuning, including evaluation of impact on complex engineering workflows
Experience rolling out AI tools across engineering teams, collaborating with vendors, and turning emerging capabilities into measurable adoption and productivity improvements
Experience developing large-scale distributed tooling, evaluation services, or observability systems with real-time constraints
Strong collaborative and interpersonal skills, with a track record of guiding and influencing others in dynamic environments

Location: Santa Clara, CA (onsite)

Salary: USD 224,000 - 356,500 per year

Senior AI Engineer

Job Description