Product Development Engineer - Data & Yield Analysis

Advanced Micro Devices, Inc•Austin, TX

34d•Onsite

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. As an Enterprise Product Development Engineer – Data & Yield Analysis, you will play a key role in driving data‑informed decisions across next‑generation AMD microprocessors. This position sits within Product Engineering and spans New Product Introduction (NPI) through production, with a strong emphasis on yield learning, characterization interpretation, and test‑point strategy. This is an analysis‑centric role. A significant portion of the job (perhaps 40-50%) involves working directly with large, imperfect silicon test and yield datasets—extracting meaningful signals from complex, non‑curated data files and translating that data into clear conclusions, risk assessments, and recommended actions for engineering teams and leadership. This role offers meaningful technical visibility and impact, and is best suited for engineers who enjoy hands‑on analysis, fast learning cycles, and using data to drive real decisions, rather than building large data platforms or test programs. This role is based on‑site in Austin, TX. You are an engineer who enjoys working with real‑world engineering data, including datasets that are incomplete, noisy, or imperfect. You are comfortable asking the right questions, validating assumptions, and standing behind conclusions with sound reasoning and statistics. You balance speed and rigor, understand tradeoffs, and communicate clearly with engineers and managers. You are comfortable working in environments where priorities evolve, projects shift, and analysis is often needed quickly to enable decisions.

Requirements

Experience analyzing complex engineering datasets and driving conclusions from data.
Solid understanding of statistical analysis techniques (e.g., distributions, correlations, Pareto analysis, margin assessment).
Experience working with silicon test, yield, or characterization data.
Proficiency in Python, JSL, Snowflake, or similar, using code for data extraction and analysis.
Comfort working directly with large, non‑curated datasets.
Strong written and verbal communication skills.
Ability to work effectively in fast‑paced, evolving environments.
B.S. in Engineering or Physics required.

Nice To Haves

Yield learning or yield improvement experience in a semiconductor environment.
Familiarity with ATE test environments (analysis‑focused rather than test‑program development).
Experience with Dataiku, Snowflake, JMP, or similar tools.
Background in Electrical Engineering, Computer Engineering, Physics, or a related quantitative discipline.
Advanced degrees welcome.

Responsibilities

Analyze large‑scale silicon test and yield data across NPI and production phases.
Extract relevant data from broad, catch‑all test files and databases, often requiring custom parsing and validation.
Drive yield improvement plans and yield loss root‑cause analysis, identifying actionable drivers.
Define, evaluate, and validate ATE test points using statistical methods, including margin analysis and voltage guardbanding.
Interpret silicon characterization data from test content such as Scan, MBIST, CReST, and HSIO tests, accounting for frequency, process corners, and operating conditions.
Communicate analysis results, risks, and recommendations clearly to cross‑functional teams.
Collaborate with Design, Diagnostics, Test, Foundry Technology, Supply Chain, and other engineering teams to resolve product‑level issues.
Develop scripts, workflows, and lightweight tools that improve analysis speed and repeatability.