Machine Learning Engineer (ML for Chip Design)

United States·San Jose

Machine Learning EngineerData

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Quick Summary

Overview

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.

Technical Tools

Machine Learning EngineerData

Responsibilities

~1 min read

→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

~1 min read

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).
Able to work on-site 5 days a week in San Jose.