Machine Learning Engineer

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 210 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.

Skills Required

• LLM fine-tuning (SFT RLHF) & post-training
• Python & PyTorch
• Hugging Face Transformers
• Distributed GPU training/inference (vLLM SGLang CUDA Ray)
• Data pipelines & synthetic data
• Model evaluation frameworks
• Agentic AI workflows
• Hardware design languages (RTL Verilog SystemVerilog) an advantage