THE PERSON
You are neither a pure data scientist who treats performance data as opaque features nor a benchmark engineer who reports only point results. You understand enough CPU and system performance to model causality, and enough statistics to avoid false precision. You care about provenance, versioning and calibration, and you can make uncertainty understandable to engineers and executives.
KEY RESPONSIBILITIES
Design and maintain a forecasting framework that links CPU architecture, memory, I/O, power, operating-system and runtime behavior, and workload kernels.
Create performance, performance-per-watt and price-performance forecasts with confidence ranges rather than single-point estimates.
Build scenarios for uncertain competitor attributes such as frequency, core count, memory bandwidth, software maturity, launch timing and platform power.
Track prediction error by workload, competitor, forecast horizon and model version; decompose errors and tune the model as measured systems become available.
Develop normalized competitive scorecards across workload performance, efficiency and TCO, memory and I/O, software ecosystem, deployability and roadmap credibility.
Fuse benchmark results, performance counters, partner telemetry, public roadmaps, software changes and platform evidence while preserving source provenance.
Identify leading indicators that materially change the forecast and surface early risk or opportunity signals.
Partner with architecture and workload leads to design experiments that reduce the most valuable uncertainties.
Build executive-grade visualizations of deltas, confidence ranges, scenarios, drivers and forecast-versus-actual history.
Write quarterly forecast narratives and support rapid recalibration when new silicon or material evidence appears.
PREFERRED EXPERIENCE
Demonstrated experience building quantitative models used for technical or business decisions under uncertainty.
Strong programming and data-analysis skills in Python or an equivalent analytical environment.
Expertise in defining meaningful error metrics, calibration methods and sensitivity analyses for sparse or biased data.
Working knowledge of server CPU and system performance and a willingness to engage deeply with architectural causality.
Experience with reproducible data pipelines, versioning, notebooks or scripts, and source provenance.
Strong visualization, writing and presentation skills for technical and executive audiences.
Sound judgment about when a model is useful, when it is overfit and when the available evidence does not support a precise conclusion.
CPU or GPU performance prediction, pre-silicon modeling, capacity planning, forecasting or benchmark analytics.
Bayesian modeling, Monte Carlo simulation, probabilistic programming or uncertainty quantification.
Experience combining structured benchmark data with semi-structured roadmap, software-change or market evidence.
Cross-ISA or cloud-instance price-performance analysis.
Familiarity with platform economics, TCO, rack power or density.
WHY THIS OPPORTUNITY STANDS OUT
Build the quantitative engine at the center of a new technical organization.
Make uncertainty actionable instead of hiding it behind a single number.
Work with architects and workload experts who can test model assumptions against real systems.
Create a visible body of work that connects data science directly to product and architecture decisions.
ACADEMIC CREDENTIALS
Bachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field
LOCATION: Austin, Texas
This role is not eligible for visa sponsorship.
#LI-RW1
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
THE TEAM
The Competitive Advanced Performance (CAP) team is a new, visible capability focused on predicting, validating and explaining competitive server CPU performance before products reach the market. Joining now means helping define the methods, tools and operating model from the ground up.
THE ROLE
AMD is seeking a data scientist and performance engineer to own the quantitative forecasting system behind the CAP team. You will combine architecture assumptions, workload measurements, software trends, platform data and partner validation into forward-looking predictions with explicit confidence ranges. The role is accountable for model error, scenario analysis, forecast-versus-actual learning and a clear competitive narrative: where AMD is likely to lead, where it may face risk, how confident the team is and which variables could change the outcome.
THE PERSON
You are neither a pure data scientist who treats performance data as opaque features nor a benchmark engineer who reports only point results. You understand enough CPU and system performance to model causality, and enough statistics to avoid false precision. You care about provenance, versioning and calibration, and you can make uncertainty understandable to engineers and executives.
KEY RESPONSIBILITIES
Design and maintain a forecasting framework that links CPU architecture, memory, I/O, power, operating-system and runtime behavior, and workload kernels.
Create performance, performance-per-watt and price-performance forecasts with confidence ranges rather than single-point estimates.
Build scenarios for uncertain competitor attributes such as frequency, core count, memory bandwidth, software maturity, launch timing and platform power.
Track prediction error by workload, competitor, forecast horizon and model version; decompose errors and tune the model as measured systems become available.
Develop normalized competitive scorecards across workload performance, efficiency and TCO, memory and I/O, software ecosystem, deployability and roadmap credibility.
Fuse benchmark results, performance counters, partner telemetry, public roadmaps, software changes and platform evidence while preserving source provenance.
Identify leading indicators that materially change the forecast and surface early risk or opportunity signals.
Partner with architecture and workload leads to design experiments that reduce the most valuable uncertainties.
Build executive-grade visualizations of deltas, confidence ranges, scenarios, drivers and forecast-versus-actual history.
Write quarterly forecast narratives and support rapid recalibration when new silicon or material evidence appears.
PREFERRED EXPERIENCE
Demonstrated experience building quantitative models used for technical or business decisions under uncertainty.
Strong programming and data-analysis skills in Python or an equivalent analytical environment.
Expertise in defining meaningful error metrics, calibration methods and sensitivity analyses for sparse or biased data.
Working knowledge of server CPU and system performance and a willingness to engage deeply with architectural causality.
Experience with reproducible data pipelines, versioning, notebooks or scripts, and source provenance.
Strong visualization, writing and presentation skills for technical and executive audiences.
Sound judgment about when a model is useful, when it is overfit and when the available evidence does not support a precise conclusion.
CPU or GPU performance prediction, pre-silicon modeling, capacity planning, forecasting or benchmark analytics.
Bayesian modeling, Monte Carlo simulation, probabilistic programming or uncertainty quantification.
Experience combining structured benchmark data with semi-structured roadmap, software-change or market evidence.
Cross-ISA or cloud-instance price-performance analysis.
Familiarity with platform economics, TCO, rack power or density.
WHY THIS OPPORTUNITY STANDS OUT
Build the quantitative engine at the center of a new technical organization.
Make uncertainty actionable instead of hiding it behind a single number.
Work with architects and workload experts who can test model assumptions against real systems.
Create a visible body of work that connects data science directly to product and architecture decisions.
ACADEMIC CREDENTIALS
Bachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field
LOCATION: Austin, Texas
This role is not eligible for visa sponsorship.
#LI-RW1
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
