Forward Deployed AI Engineer

Forward Deployed AI Engineer

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.

We are hiring Forward Deployed AI Engineer to build the prototypes, tools, integrations, and evaluation loops that turn AI opportunities into working engineering systems. This role is hands-on and field-facing: you will work directly with internal engineering teams and strategic partners to understand workflows, connect AI systems to real tools, and prove what works through measurable results.

The role helps scale the voice of the customer by making technical needs concrete. You will convert stakeholder pain points into scoped experiments, build the first working versions, integrate with engineering environments, and feed what you learn back into research and platform teams.

You are a pragmatic engineer who can build quickly without losing sight of reliability, correctness, and stakeholder value. You are comfortable in unfamiliar codebases and toolchains. You can work with researchers on methods, with hardware and software teams on constraints, and with partner-facing teams on demos and technical narratives.

Key responsibilities include:

• Build forward-deployed AI prototypes and production-oriented workflows for engineering teams and strategic partners.
• Integrate LLMs and agents with real engineering tools such as compilers, profilers, test harnesses, simulators, validation systems, knowledge bases, dashboards, and ticketing systems.
• Translate ambiguous stakeholder needs into working software, clear evals, and measurable proof points.
• Design and maintain evaluation harnesses, benchmark workflows, logging, dashboards, and reproducible experiment pipelines.
• Partner with AI researchers to test new methods in realistic environments and identify where models, tools, prompts, rewards, or data need improvement.
• Support strategic technical engagements by preparing demos, prototypes, technical documentation, and implementation plans.
• Improve adoption by making tools easy for engineers to run, inspect, debug, and trust.
• Capture reusable patterns from field deployments and feed them into shared infrastructure, libraries, and best practices.

Technical focus areas include:

• AI agent integration with engineering workflows, tools, and data sources.
• Rapid prototyping with clear paths toward reliability, observability, and maintainability.
• Evaluation-driven development using tests, benchmarks, correctness checks, human review, and performance metrics.
• Systems and performance engineering across GPU, CPU, distributed systems, compilers, or hardware-adjacent workflows.
• Developer experience for AI tools used by expert engineers.

Required qualifications include:

• Strong software engineering skills in Python and at least one systems or application language such as C++, C, TypeScript, HIP, or CUDA.
• Experience building AI-enabled applications, ML systems, developer tools, automation systems, or technical prototypes for expert users.
• Ability to integrate with complex tools, APIs, logs, build systems, test systems, and internal engineering environments.
• Strong debugging, systems thinking, and ability to move from ambiguous requirements to working software.
• Clear written and verbal communication with engineering stakeholders, researchers, partner teams, and leadership.

Preferred experience includes:

• Experience with LLM agents, tool calling, retrieval, code generation, automated debugging, evaluation frameworks, or ML workflow orchestration.
• Experience with GPU/CPU performance engineering, ROCm/HIP, CUDA, PyTorch, JAX, TensorFlow, compilers, profilers, or distributed training/inference.
• Experience with hardware design, verification, simulation, firmware, EDA tools, or hardware/software co-design is a plus.
• Experience in customer engineering, solutions engineering, forward-deployed engineering, technical partnerships, or internal platform teams.
• Track record of shipping tools that engineers actually use in fast-moving, cross-functional environments.

Academic credentials include:

• 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 strong applied research or systems experience.

Location: Santa Clara, CA

This role is not eligible for visa sponsorship.