About the Role

Our partner company is looking for a senior, product-driven Machine Learning Engineer with deep expertise in Large Language Models (LLMs) and a strong track record of deploying real-world ML systems. In this company, you’ll work at the frontier of AI and semiconductor design—collaborating closely with AI scientists to translate cutting-edge research into production-ready systems. This is a hands-on, high-impact role where you'll contribute to the core intelligence of our platform and help deliver AI-native tools that empower chip designers.

Core Responsibilities

● Fine-tune, evaluate, and deploy LLMs and foundation models optimized for specific chip engineering use cases.

● Build scalable inference infrastructure, agentic workflows and RAG systems.

● Apply techniques such as batching, caching, quantization, and streaming to support low-latency interaction.

● Partner with AI scientists to translate cutting-edge research into robust, product-grade systems.

● Iterate on experimental techniques, validate hypotheses, and drive integration into the product.

Required Technical Skills:

● 5–10 years of ML engineering experience, with a focus on shipping ML solutions to production

● 3+ years of hands-on experience with state of the art LLMs

● Familiarity with agentic workflows, orchestration frameworks (LangChain, LlamaIndex)

● Strong skills in Python and PyTorch ecosystem

● Experience with vector search, semantic embeddings, and RAG pipelines

● Familiarity with prompt tuning, fine-tuning, and evaluation frameworks

● Deployment experience in AWS using SageMaker, Bedrock, vLLM or Triton

● Understanding of scalable, production-grade ML service architecture Preferred Qualifications

● Experience working directly with AI/ML researchers or scientists

● Background in chip design, physical design tools, or EDA software

● Previous startup experience or work in fast-moving cross-functional teams

● Open-source contributions to ML infrastructure or libraries

What We Offer

● The opportunity to bring AI research into practice—not in a lab, but in the hands of engineers building the future

● High-trust, low-ego environment with strong collaboration between science, product, and engineering

● A team that values pragmatism, curiosity, and real-world impact

● Competitive salary and early-stage equity