Research Engineer, ML for EDA

San Josefull time

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On Site

$150k

H1B: no

Alpha Design AI Inc Research Engineer at Alpha Design AI applies machine learning to electronic design automation (EDA) focusing on power, performance, and area tradeoffs in chip design. Leads research projects from problem definition through production integration in the ChipAgents platform.

Responsibilities

Lead research projects that apply machine learning to power, performance, and area problems in chip design, from problem definition to production.
Build datasets and benchmarks from EDA tool flows across a range of designs and technologies.
Design, train, and evaluate ML models on hardware design data.
Form clear hypotheses, set strong baselines, and distinguish meaningful improvements from benchmark noise.
Work with engineering and product teams to bring research results into the ChipAgents platform.
Write well-architected, maintainable code that others can build on.

Qualification

MS or PhD in Computer ScienceSelf-directed and ambitiousPublications at EDA venues such as DACIndustry experience in physical designExperience with LLMs or agent systemsOwn research problems end to end

Required

MS or PhD in Computer Science, Electrical or Computer Engineering, or a related field, or equivalent research experience in industry.
Strong ML fundamentals and hands-on experience designing, training, and debugging deep learning models in PyTorch or similar platforms.
Working knowledge of the digital design flow, from RTL through synthesis, place and route, and timing and power analysis.
Experience running and scripting EDA tools, commercial or open source.
Strong Python software engineering skills and a record of writing research code that others build on.
Ability to identify the crux of an ambiguous research problem, choose the experiments that matter, and explain results clearly to ML and hardware engineers.
Self-directed and ambitious, with strong ownership and good judgment about when work is ready to ship.

Preferred

Publications at EDA venues such as DAC, ICCAD, MLCAD, or ISPD, or at ML venues such as NeurIPS, ICML, or ICLR.
Prior work applying ML to EDA or chip design problems.
Experience with ML on graphs or other structured data.
Industry experience in physical design, timing closure, or power analysis.
Experience with LLMs or agent systems.
Experience running large-scale training or data generation on compute clusters.
Your research ships to semiconductor companies and gets tested on their designs as well as public benchmarks.
Own research problems end to end, with software engineers who take your work into production and customer environments so you can stay focused on research.

Benefits

Dental
Vision
401(k)
PTO
Equity/RSU

Company

Alpha Design AI Inc

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Source: ashby

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