AI Research Scientist — Agentic AI and Foundation Models

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.

Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career.

THE ROLE:

We are seeking an AI Research Scientist to advance language models and intelligent agents for complex technical and research tasks. You will explore how models acquire domain knowledge, reason, use tools, and learn from interaction with real development environments

The role spans model training, agent design, and rigorous evaluation, with applications in software development, hardware design and verification, performance optimization, and automated technical discovery. Research may include pre-training, domain-adaptive continued pre-training, post-training, and reinforcement learning, alongside inference-time reasoning and search.

You will own projects from initial research questions through working systems and demonstrated impact, delivering advances in models and agents that solve increasingly complex technical problems, generalize across projects and domains, and measurably improve the quality and productivity of software and hardware development.

THE PERSON:

You combine research depth with strong engineering skills. You enjoy turning ambiguous problems into experiments that improve model and agent capabilities. You can identify whether limitations stem from model knowledge, reasoning, context, or tools, and develop approaches that address them. You value both original research and measurable results in practical settings.

KEY RESPONSIBILITIES:

• Research methods for reasoning, planning, tool use, memory, and reliable execution of multistep tasks.
• Develop pre-training and post-training approaches that strengthen models’ technical knowledge, coding abilities, and problem-solving capabilities.
• Build learning pipelines using curated corpora, demonstrations, synthetic data, agent trajectories, and execution feedback.
• Apply reinforcement learning and other adaptation methods to improve agent performance in executable environments.
• Investigate inference-time reasoning and search to improve solution quality and computational efficiency.
• Develop agents for software and hardware workflows, including code generation, RTL optimization, debugging, verification, and performance analysis.
• Create benchmarks that measure correctness, generalization, and sustained performance across unfamiliar projects.
• Collaborate with software and hardware teams to translate research into useful capabilities.
• Publish research and share findings through technical reports, reusable systems, and working demonstrations.
• PREFERRED EXPERIENCE: A strong publication record in language models, AI agents, reinforcement learning, code intelligence, automated reasoning, or related areas, including venues such as NeurIPS, ICML, ICLR, ACL, and EMNLP.
• Hands-on experience with LLM pre-training, continued pre-training, or post-training, including supervised fine-tuning and reinforcement learning.
• Experience building agents that interact with tools and executable environments.
• Experience developing training datasets, generating synthetic data, or learning from agent trajectories and verifiable feedback.
• Strong Python and deep learning framework skills, with experience running reproducible experiments.
• Familiarity with distributed training, large-scale evaluation, or efficient inference.
• Experience applying AI to software development, hardware design, scientific computing, or optimization.
• Familiarity with RTL, simulation, formal verification, compilers, or GPU kernels is a plus.
• ACADEMIC CREDENTIALS: Master’s or PhD in Computer Science, Machine Learning, Electrical Engineering, or a related field preferred. Candidates with equivalent research experience and demonstrated contributions are also encouraged to apply.

Responsibilities

THE ROLE:

We are seeking an AI Research Scientist to advance language models and intelligent agents for complex technical and research tasks. You will explore how models acquire domain knowledge, reason, use tools, and learn from interaction with real development environments

The role spans model training, agent design, and rigorous evaluation, with applications in software development, hardware design and verification, performance optimization, and automated technical discovery. Research may include pre-training, domain-adaptive continued pre-training, post-training, and reinforcement learning, alongside inference-time reasoning and search.

You will own projects from initial research questions through working systems and demonstrated impact, delivering advances in models and agents that solve increasingly complex technical problems, generalize across projects and domains, and measurably improve the quality and productivity of software and hardware development.

THE PERSON:

You combine research depth with strong engineering skills. You enjoy turning ambiguous problems into experiments that improve model and agent capabilities. You can identify whether limitations stem from model knowledge, reasoning, context, or tools, and develop approaches that address them. You value both original research and measurable results in practical settings.

KEY RESPONSIBILITIES:

• Research methods for reasoning, planning, tool use, memory, and reliable execution of multistep tasks.
• Develop pre-training and post-training approaches that strengthen models’ technical knowledge, coding abilities, and problem-solving capabilities.
• Build learning pipelines using curated corpora, demonstrations, synthetic data, agent trajectories, and execution feedback.
• Apply reinforcement learning and other adaptation methods to improve agent performance in executable environments.
• Investigate inference-time reasoning and search to improve solution quality and computational efficiency.
• Develop agents for software and hardware workflows, including code generation, RTL optimization, debugging, verification, and performance analysis.
• Create benchmarks that measure correctness, generalization, and sustained performance across unfamiliar projects.
• Collaborate with software and hardware teams to translate research into useful capabilities.
• Publish research and share findings through technical reports, reusable systems, and working demonstrations.
• PREFERRED EXPERIENCE: A strong publication record in language models, AI agents, reinforcement learning, code intelligence, automated reasoning, or related areas, including venues such as NeurIPS, ICML, ICLR, ACL, and EMNLP.
• Hands-on experience with LLM pre-training, continued pre-training, or post-training, including supervised fine-tuning and reinforcement learning.
• Experience building agents that interact with tools and executable environments.
• Experience developing training datasets, generating synthetic data, or learning from agent trajectories and verifiable feedback.
• Strong Python and deep learning framework skills, with experience running reproducible experiments.
• Familiarity with distributed training, large-scale evaluation, or efficient inference.
• Experience applying AI to software development, hardware design, scientific computing, or optimization.
• Familiarity with RTL, simulation, formal verification, compilers, or GPU kernels is a plus.
• ACADEMIC CREDENTIALS: Master’s or PhD in Computer Science, Machine Learning, Electrical Engineering, or a related field preferred. Candidates with equivalent research experience and demonstrated contributions are also encouraged to apply.

Qualifications