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Forward Deployed AI Engineer

Job in Santa Clara, Santa Clara County, California, 95053, USA
Listing for: AMD
Full Time position
Listed on 2026-07-13
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), AI Reliability/ Performance Engineer, Software Engineer
Salary/Wage Range or Industry Benchmark: 180000 - 240000 USD Yearly USD 180000.00 240000.00 YEAR
Job Description & How to Apply Below

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 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.

The Person

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 tool chains. 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
  • 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
  • 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

Strong software engineering skills in Python and at least one systems or application language such as C++, C, Type Script, 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
  • 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, Tensor Flow, compilers,…
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