AMD

4 weeks ago

AI Research Scientist, Hardware AI Systems

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 4 hours agoStatus: Live

Job Summary

Develop AI systems that generalize across hardware engineering contexts, including different SoC programs, toolchains, and process nodes. Work on metareasoning, representation learning across RTL/verification/physical design flows, and learning with high- or mixed-latency rewards typical of silicon signoff. Collaborate with methodology and silicon teams to turn one-off wins into reusable models, benchmarks, and transfer protocols, while publishing and maintaining evidence-backed generalization standards.

Location: Santa Clara

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•Develop AI research that generalizes across hardware engineering contexts without bespoke retraining for every program.
•Research transfer and multi-task learning for engineering agents across IP blocks, tools, and program generations.
•Develop metareasoning policies for budgeting expensive EDA steps, selecting abstractions, and recovering from tool or data failures.
•Build cross-program benchmarks and datasets to expose generalization gaps and track progress over time.
•Collaborate with RL scientists on reward shaping and credit assignment, and publish with evidence-backed generalization claims.

Pay and Benefits

Salary: USD 178,500 - 306,000 annually

Key Requirements

•PhD in Computer Science, Machine Learning, or a related field (strongly preferred).
•Deep technical expertise and proven research experience in machine learning (ML), emphasizing transfer learning, meta-learning, intelligent agents, and long-horizon reinforcement learning (RL).
•Exposure to hardware development, including RTL, verification, physical design (PD), or bring-up, or willingness to ramp with embedded experts.
•Experience with Large Language Models (LLM) tool use, planning, or hierarchical control in real toolchains.
•Ability to think in distribution shift, domain adaptation, and structured priors for problem formulation.
•Develop metareasoning policies for budgeting expensive EDA steps, selecting abstractions, and recovering from tool or data failures

Education:PhD / Doctorate in Computer Science, Machine Learning, or related field

Skills:CollaborationResearch rigorEvidence-backed decision-making

Languages:English

Tech Stack:Machine Learning (ML)Transfer learningMeta-learningIntelligent agentsReinforcement learning (RL)MetareasoningRepresentation learningRTLVerificationPhysical design (PD)EDASoCToolchainsProcess nodesLarge Language Models (LLM)Hierarchical controlPlanningReward shapingCredit assignment

Company Brief

AMD

Designs and produces semiconductor products including CPUs, GPUs, and adaptive SoCs for consumer, enterprise, and embedded markets, competing across PCs, data centers, and gaming industries.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1969

Glassdoor: 3.9

Website LinkedIn

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AMD