layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Forward Deployed AI Research Scientist at AMD, Santa Clara, CA

Work with internal and external stakeholders to identify high-value AI opportunities in compute, systems, software, and hardware engineering workflows.
Translate partner and customer needs into research questions, technical plans, evaluation criteria, and prototype paths.
Prototype and evaluate modern AI methods for engineering tasks, including agentic workflows, tool use, code generation, debugging, optimization, retrieval, and learning from feedback.
Partner with research and engineering teams to define metrics, evals, benchmarks, and acceptance criteria for AI-assisted workflows.
Build technical narratives, demos, and decision materials that help senior stakeholders understand tradeoffs, progress, risks, and strategic value.
Support strategic deals and collaborations by serving as a credible technical bridge between business goals and AI engineering reality.
Capture recurring customer and partner needs and turn them into reusable insights for product, research, platform, and engineering roadmaps.
Mentor teams on practical AI methods and help raise the quality of AI experimentation, evaluation, and communication across the organization.
TECHNICAL FOCUS AREAS: Applied AI research for engineering workflows where correctness, performance, latency, reliability, and explainability matter.
Agentic systems that use tools, tests, profilers, simulators, validation systems, and structured feedback.
Evaluation design for ambiguous or high-value tasks, including objective graders, human-in-the-loop review, and LLM-as-judge methods where appropriate.
AI systems for code understanding, code generation, optimization, debugging, knowledge retrieval, and workflow automation.
Technical-business translation for strategic customers, partners, engineering leaders, and cross-functional teams.
REQUIRED QUALIFICATIONS: Strong background in AI, machine learning, systems, or applied research, with ability to turn ideas into prototypes and measurable experiments.
Experience with modern generative AI methods, LLMs, agents, tool use, retrieval, post-training, evaluation, or applied ML systems.
Strong programming ability in Python and familiarity with ML frameworks such as PyTorch, JAX, TensorFlow, or similar.
Ability to work with technical stakeholders across software, hardware, infrastructure, research, product, and business teams.
Excellent communication skills, including the ability to explain complex AI and systems topics clearly to both technical and executive audiences.
PREFERRED EXPERIENCE: Experience in forward-deployed engineering, customer engineering, applied research, technical partnerships, solutions architecture, or strategic technical programs.
Experience with GPU systems, CPU performance, compilers, distributed training/inference, hardware/software co-design, or performance engineering.
Experience designing benchmarks, evals, experiment platforms, or metrics for AI systems.
Publications, open-source contributions, technical blogs, demos, or shipped AI systems are a plus.
Familiarity with semiconductor, datacenter, AI infrastructure, or high-performance computing environments is a strong plus.
ACADEMIC CREDENTIALS: Bachelor's degree is required preferrably in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience. Master's preferred; PhD is a plus, especially in AI, ML systems, computer systems, GPU computing, or hardware/software co-design.

Responsibilities

THE ROLE:

We are hiring

Forward Deployed AI Research Scientist to help bring advanced AI capabilities into high-impact engineering and customer-facing workflows. This role combines applied AI research, technical strategy, prototyping, and field-facing collaboration. You will work closely with internal engineering teams and strategic external partners to identify valuable problems, shape them into tractable AI research and engineering efforts, and drive them toward measurable outcomes.

The role is designed to scale a technical-business bridge across AMD: translating customer and partner needs into AI research directions, translating AI capabilities into engineering value, and helping teams align on what can be built, measured, and delivered.,

THE PERSON:

You are a research-minded technical leader who can operate in ambiguous, high-stakes environments. You can speak credibly with AI researchers, software engineers, hardware engineers, executives, and partner teams. You are equally comfortable reading papers, shaping experiments, building prototypes, writing crisp technical narratives, and helping stakeholders understand what matters.

KEY RESPONSIBILITIES:

Work with internal and external stakeholders to identify high-value AI opportunities in compute, systems, software, and hardware engineering workflows.
Translate partner and customer needs into research questions, technical plans, evaluation criteria, and prototype paths.
Prototype and evaluate modern AI methods for engineering tasks, including agentic workflows, tool use, code generation, debugging, optimization, retrieval, and learning from feedback.
Partner with research and engineering teams to define metrics, evals, benchmarks, and acceptance criteria for AI-assisted workflows.
Build technical narratives, demos, and decision materials that help senior stakeholders understand tradeoffs, progress, risks, and strategic value.
Support strategic deals and collaborations by serving as a credible technical bridge between business goals and AI engineering reality.
Capture recurring customer and partner needs and turn them into reusable insights for product, research, platform, and engineering roadmaps.
Mentor teams on practical AI methods and help raise the quality of AI experimentation, evaluation, and communication across the organization.
TECHNICAL FOCUS AREAS: Applied AI research for engineering workflows where correctness, performance, latency, reliability, and explainability matter.
Agentic systems that use tools, tests, profilers, simulators, validation systems, and structured feedback.
Evaluation design for ambiguous or high-value tasks, including objective graders, human-in-the-loop review, and LLM-as-judge methods where appropriate.
AI systems for code understanding, code generation, optimization, debugging, knowledge retrieval, and workflow automation.
Technical-business translation for strategic customers, partners, engineering leaders, and cross-functional teams.
REQUIRED QUALIFICATIONS: Strong background in AI, machine learning, systems, or applied research, with ability to turn ideas into prototypes and measurable experiments.
Experience with modern generative AI methods, LLMs, agents, tool use, retrieval, post-training, evaluation, or applied ML systems.
Strong programming ability in Python and familiarity with ML frameworks such as PyTorch, JAX, TensorFlow, or similar.
Ability to work with technical stakeholders across software, hardware, infrastructure, research, product, and business teams.
Excellent communication skills, including the ability to explain complex AI and systems topics clearly to both technical and executive audiences.
PREFERRED EXPERIENCE: Experience in forward-deployed engineering, customer engineering, applied research, technical partnerships, solutions architecture, or strategic technical programs.
Experience with GPU systems, CPU performance, compilers, distributed training/inference, hardware/software co-design, or performance engineering.
Experience designing benchmarks, evals, experiment platforms, or metrics for AI systems.
Publications, open-source contributions, technical blogs, demos, or shipped AI systems are a plus.
Familiarity with semiconductor, datacenter, AI infrastructure, or high-performance computing environments is a strong plus.
ACADEMIC CREDENTIALS: Bachelor's degree is required preferrably in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience. Master's preferred; PhD is a plus, especially in AI, ML systems, computer systems, GPU computing, or hardware/software co-design.

Forward Deployed AI Research Scientist

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WHAT YOU DO AT AMD CHANGES EVERYTHING

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