Sr. Forward Deployed AI Engineer

Advanced Micro Devices, Inc•Santa Clara, CA

80d•Hybrid

About The Position

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're looking for a Forward Deployed Research Engineer to build, evaluate, and deploy cutting-edge AI systems that solve complex engineering challenges for enterprise customers. This is not a traditional Solutions Engineer or prompt engineering role. You'll combine deep software engineering, applied AI research, and customer engagement to develop production-ready AI systems using modern LLMs, reinforcement learning, agentic workflows, and post-training techniques. You'll work directly with customers, researchers, and product teams to transform ambiguous engineering problems into scalable AI solutions. From rapid prototyping to production deployment, you'll help shape both customer success and the evolution of our AI platform. What Makes This Role Unique Build AI systems that solve real-world engineering problems—not proof-of-concepts. Work across applied AI research, software engineering, and customer deployment. Partner directly with enterprise engineering teams to define, build, and validate AI solutions. Influence future AI products by translating customer challenges into reusable platform capabilities. Own projects end-to-end—from technical discovery and experimentation through deployment and measurable impact.

Requirements

Strong software engineering experience in Python and at least one systems language such as C++, Rust, C, TypeScript, CUDA, or HIP.
Proven experience building production AI or machine learning systems beyond prompt engineering or API integrations.
Hands-on experience with one or more of the following: LLM post-training, Reinforcement Learning (RLHF, PPO, DPO, GRPO, or similar), Preference Optimization, Reward Modeling, Model or Agent Evaluation, AI Training or Inference Infrastructure
Deep understanding of transformer-based LLMs, inference, fine-tuning, retrieval, evaluation, and agent architectures.
Experience designing reproducible evaluation frameworks using benchmarks, testing, and measurable performance metrics.
Strong systems thinking, debugging, and problem-solving skills.
Experience translating ambiguous technical requirements into scalable production solutions.
Ability to work directly with customers and technical stakeholders to drive solution design, implementation, and adoption.
Excellent written and verbal communication skills with engineers, researchers, customers, and executive leadership.

Nice To Haves

Experience training, fine-tuning, or post-training foundation models or open-source LLMs.
Experience with PyTorch, Hugging Face, JAX, TensorFlow, Ray, vLLM, or distributed AI infrastructure.
Experience building AI agents, retrieval systems, tool calling, workflow orchestration, or automated code generation.
Experience with GPU optimization, CUDA, distributed systems, or high-performance computing.
Background in forward-deployed engineering, applied AI research, customer engineering, or solutions architecture.
Demonstrated success delivering AI solutions from customer discovery through deployment with measurable business impact.

Responsibilities

Partner with customer engineering teams to understand workflows, technical challenges, and opportunities for AI-driven automation.
Design, build, and deploy production-grade AI applications, LLM agents, and engineering automation solutions.
Develop evaluation frameworks, benchmarks, testing environments, and data pipelines to measure model quality and business impact.
Build and evaluate LLM post-training solutions, including supervised fine-tuning, reinforcement learning, preference optimization, and reward modeling.
Diagnose and optimize AI system performance across models, data, infrastructure, orchestration, and tooling.
Collaborate with AI researchers to validate new models and techniques in production environments.
Present technical solutions, architecture, and implementation strategies to engineering leaders, customers, and executive stakeholders.
Transform recurring customer use cases into reusable AI infrastructure, frameworks, and platform capabilities.