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AMD

Designs chips for computers and AI systems

Senior Technical Program Manager-CPU Cores

Program ManagementConfirmed live in the last 24 hours
Full-timeFishkill, NY, USANo salary listed

About the Job

Requirements

  • Significant experience in CPU or SoC development, with hands-on background in functional verification, RTL design, or physical design/implementation.
  • Proven TPM or technical program leadership delivering complex, cross-functional programs end-to-end in ambiguous, fast-changing environments.
  • Working knowledge of AI/ML or data science techniques and demonstrated experience applying them, or overseeing their application, to hardware design, verification, or EDA-related problems.
  • Solid understanding of core development flows and tooling, including simulation, regression, coverage collection and debug for verification; RTL/synthesis flows for design; and timing, place-and-route, and physical verification for physical design, sufficient to scope and prioritize AI initiatives across these domains.
  • Demonstrated experience with program planning tools such as Jira, Confluence, or MS Project, or equivalent, and agile delivery practices.
  • Direct hands-on experience with Python and common machine-learning frameworks/tooling such as TensorFlow, PyTorch, or Scikit-learn as applied to engineering or EDA data.
  • Experience working with large datasets, relational databases, and visualization tools such as Pandas, SQL, or Tableau in a silicon design or verification context.

Responsibilities

  • Drive the definition and deployment of AI-enabled program strategies across verification, RTL design, and physical design within x86 core development.
  • Identify and prioritize opportunities to apply machine learning, data analytics, and automation to engineering workflows, including predictive coverage modeling, test/testbench generation, timing and congestion prediction, floorplan and place-and-route optimization, RTL code assistance, anomaly detection in regression and bring-up data, and root-cause and failure triage.
  • Develop program plans, define milestones, manage inter-team dependencies, and maintain program key performance indicators tied to verification velocity, coverage closure, power, performance, and area targets, and design-cycle schedule adherence.
  • Coordinate cross-functional work across architecture, RTL/design, verification, design for test, physical design, performance modeling, and internal tooling/data engineering teams to build and scale intelligent workflows.
  • Partner with electronic design automation and internal tooling teams to define infrastructure, data pipelines, and compute requirements for collecting, cleaning, and analyzing simulation, coverage, timing, and layout data in support of AI/ML initiatives.
  • Evaluate emerging AI tools and solutions, including open-source, EDA-vendor, and internally built tools, and drive build-versus-buy recommendations.
  • Identify, track, and mitigate program risks and issues; escalate when needed and drive corrective action with stakeholders.
  • Facilitate communication and alignment across design, verification, and physical design teams, as well as with EDA partners and tool vendors; serve as the primary program point of contact for AI adoption initiatives.
  • Track and report measurable improvements in engineering efficiency and design quality, including verification velocity, coverage efficiency, bug/defect escape rate, and design-cycle throughput, resulting from AI-driven methods.
  • Mentor and coordinate with engineering leads and other program managers to scale successful AI adoption pilots into repeatable, organization-wide practices.
  • Support technical tradeoff and prioritization discussions by preparing data-backed analyses, schedules, and impact assessments for engineering and executive leadership.
  • Proactively identify risks, execution gaps, and systemic bottlenecks in adopting new methodologies; implement mitigation plans and hold teams accountable to closure.
  • Present technical progress, risks, and tradeoffs clearly to leadership.

Desired Qualifications

  • A master's or bachelor's degree in Computer Engineering, Computer Science, or Electrical Engineering.
  • Excellent stakeholder management, written and verbal communication, and presentation skills, with comfort engaging at engineering and executive levels.
  • Prior experience developing or deploying machine-learning models specifically for EDA, verification, or physical-design applications.
  • Familiarity with model training/monitoring tools such as TensorBoard, MLflow, or Weights & Biases, and reinforcement-learning or active-learning approaches in constrained engineering domains.
  • Experience spanning more than one of verification, RTL design, and physical design, or experience leading programs that cut across multiple core-development disciplines.

About the Company

AMD

Designs chips for computers and AI systems

AMD designs processors, graphics chips and AI accelerators for computer makers, cloud operators and businesses that build electronic devices. Its Ryzen processors power personal computers, while EPYC processors handle work in data centers and Radeon chips render graphics. Instinct accelerators run AI models, paired with software that developers use to train models and put them into production. AMD sells chips and related computing systems through equipment makers, distributors and retail channels, and contracts with outside foundries to fabricate its semiconductor designs. It was founded in 1969 and has its headquarters in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1969

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