Job description

WHAT YOU DO AT AMD CHANGES EVERYTHING At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. THE ROLE: We are hiring Applied AI Engineers to work directly with hardware and software engineering teams on high-priority AI-for-engineering efforts. This role turns difficult engineering workflows into AI-assisted systems that generate candidates, validate correctness, measure quality, and help engineers move faster on real product problems. You will work across hardware-adjacent domains such as design optimization, verification, simulation, firmware, performance debugging, routing, issue triage, and compute library optimization. The work is practical, measured, and deeply collaborative with domain experts. THE PERSON: You are a builder who likes ambiguous, high-impact engineering problems. You can sit with domain experts, understand the real workflow, identify what can be measured, build the tool or agent loop, and iterate until it saves engineering time or improves a concrete metric. You value correctness, validation, and workflow fit as much as model capability. KEY RESPONSIBILITIES: • Build applied AI workflows for top hardware and software engineering priorities, including optimization, verification, debugging, simulation, and automation workflows. • Convert manual engineering processes into structured tasks with inputs, candidate generation, validation, scoring, logging, and reproducible comparisons. • Partner with domain owners to define success metrics such as correctness, performance, resource usage, quality, latency, coverage, engineer time saved, or issue classification accuracy. • Develop tools that let AI agents use compilers, simulators, formal checks, profilers, benchmark harnesses, ticket systems, and engineering knowledge sources. • Build human-in-the-loop workflows for tasks where annotated data, expert judgment, or subjective triage is required. • Improve model and agent performance through better prompts, tool interfaces, retrieval, evaluation datasets, graders, and structured feedback. • Work with research and infrastructure teams to generalize repeated patterns into reusable platforms, harnesses, dashboards, and data assets. • Communicate progress through measurable outcomes, demos, concise technical writeups, and clear stakeholder updates. TECHNICAL FOCUS AREAS: • AI-assisted hardware design and software optimization workflows. • Automated validation using tests, benchmarks, formal checks, simulators, and domain-specific graders. • Agentic systems for code generation, debugging, root-cause analysis, issue triage, and engineering workflow automation. • Evaluation design for tasks where ground truth may be sparse, expensive, or partially subjective. • Tooling that connects LLMs and agents to real engineering systems while preserving reliability and auditability. • Collaboration with domain experts to turn expert workflows into repeatable AI-assisted processes. PREFERRED QUALIFICATIONS: • Strong software engineering experience in Python and at least one systems language such as C++, C, HIP, CUDA, or Rust. • Experience building applied AI, ML, agentic, automation, or developer tooling systems for technical users. • Ability to work with complex engineering tools, logs, tests, benchmark harnesses, and validation workflows. • Strong debugging and root-cause analysis skills across software systems, AI workflows, or hardware-adjacent tooling. • Excellent collaboration and communication skills with domain experts, engineering leads, research teams, and program stakeholders. PREFERRED EXPERIENCE: • Experience with hardware design or verification workflows, including RTL, Verilog/SystemVerilog, simulation, formal verification, EDA tools, timing analysis, or power/performance/area tradeoffs. • Experience with GPU/CPU performance engineering, compiler tooling, profilers, kernel optimization, ROCm/HIP, CUDA, or benchmarking. • Experience designing evaluation datasets, graders, dashboards, leaderboards, or experiment tracking systems. • Familiarity with LLM agents, tool use, retrieval, LLM-as-judge workflows, RL, or post-training methods. • Experience working with internal engineering customers, forward-deployed teams, or cross-functional product engineering programs. EDUCATION Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience. Master's preferred; PhD is a plus for candidates with relevant AI, systems, EDA, or hardware/software co-design experience. LOCATION : Santa Clara, CA #LI-BW1 Benefits offered are described: AMD benefits at a glance . AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

Requirements

Must-haves

Build applied AI workflows for top hardware and software engineering priorities, including optimization, verification, debugging, simulation, and automation workflows.
Convert manual engineering processes into structured tasks with inputs, candidate generation, validation, scoring, logging, and reproducible comparisons.
Partner with domain owners to define success metrics such as correctness, performance, resource usage, quality, latency, coverage, engineer time saved, or issue classification accuracy.
Develop tools that let AI agents use compilers, simulators, formal checks, profilers, benchmark harnesses, ticket systems, and engineering knowledge sources.
Build human-in-the-loop workflows for tasks where annotated data, expert judgment, or subjective triage is required.
Improve model and agent performance through better prompts, tool interfaces, retrieval, evaluation datasets, graders, and structured feedback.
Work with research and infrastructure teams to generalize repeated patterns into reusable platforms, harnesses, dashboards, and data assets.
Communicate progress through measurable outcomes, demos, concise technical writeups, and clear stakeholder updates.
AI-assisted hardware design and software optimization workflows.
Automated validation using tests, benchmarks, formal checks, simulators, and domain-specific graders.
Agentic systems for code generation, debugging, root-cause analysis, issue triage, and engineering workflow automation.
Evaluation design for tasks where ground truth may be sparse, expensive, or partially subjective.
Tooling that connects LLMs and agents to real engineering systems while preserving reliability and auditability.
Collaboration with domain experts to turn expert workflows into repeatable AI-assisted processes.

Nice-to-haves

Strong software engineering experience in Python and at least one systems language such as C++, C, HIP, CUDA, or Rust.
Experience building applied AI, ML, agentic, automation, or developer tooling systems for technical users.
Ability to work with complex engineering tools, logs, tests, benchmark harnesses, and validation workflows.
Strong debugging and root-cause analysis skills across software systems, AI workflows, or hardware-adjacent tooling.
Excellent collaboration and communication skills with domain experts, engineering leads, research teams, and program stakeholders.
Experience with hardware design or verification workflows, including RTL, Verilog/SystemVerilog, simulation, formal verification, EDA tools, timing analysis, or power/performance/area tradeoffs.
Experience with GPU/CPU performance engineering, compiler tooling, profilers, kernel optimization, ROCm/HIP, CUDA, or benchmarking.
Experience designing evaluation datasets, graders, dashboards, leaderboards, or experiment tracking systems.
Familiarity with LLM agents, tool use, retrieval, LLM-as-judge workflows, RL, or post-training methods.
Experience working with internal engineering customers, forward-deployed teams, or cross-functional product engineering programs.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonrustmlllmmachine learning

Platform: amd
Effort: medium