B.S., M.S., or PhD in Computer Science, Electrical Engineering, or related field.
2-10 years as an ML engineer or recent PhD graduate.
Hands-on experience with LLM training or fine-tuning using Python and PyTorch.
Familiarity with distributed GPU training and inference technologies.
Willingness to work on-site 5 days a week in San Jose.
Responsibilities
Train and fine-tune large language models for chip design tasks.
Build and maintain data pipelines for data collection and cleaning.
Design evaluation methods to measure model accuracy and performance.
Collaborate with research scientists and engineers to develop AI agents.
Utilize large GPU clusters for experiments and model behavior analysis.
Benefits
Opportunity to work with cutting-edge AI technologies.
Collaborative environment with experts in chip design and AI.
Hands-on experience with large-scale GPU infrastructure.
Potential for impactful contributions to production AI systems.
Full Job Description
Responsibilities
Train, fine-tune, and post-train large language models for chip design and verification tasks using supervised fine-tuning, human-feedback training, and reinforcement learning.
Build and maintain data pipelines, including data collection, cleaning, and synthetic data creation.
Design strong evaluation methods to measure model accuracy and AI agent performance.
Work with research scientists, systems engineers, and chip-design experts to turn research ideas into production AI agents.
Use large GPU clusters to run experiments, study model behavior, and improve AI code-generation workflows.
Requirements
B.S., M.S., or PhD in Computer Science, Electrical Engineering, or a related field.
Alternatively, 2-10 years as an ML engineer, or a recent PhD graduate.
Hands-on experience training or fine-tuning LLMs using Python and PyTorch.
Familiarity with distributed GPU training and inference (vLLM, SGLang, CUDA, Ray).
