Principal Applied ML Engineer (AI Engineering)

TL;DR

Principal Applied ML Engineer (AI Engineering): Designing and implementing AI agents that enhance productivity across the semiconductor design lifecycle, with an accent on building robust evaluation frameworks to measure agent performance and reliability. Focus on optimizing system performance across latency, cost, reliability, and scalability dimensions within hirify.global’s ChipStack SuperAgent ecosystem.

Location: San Jose, CA. Must be based in the United States.

Salary: $136,500 to $253,500 (California).

Company

hirify.global Design Systems is the leading provider of design automation tools for electronic and intelligent systems design.

What you will do

• Design and implement scalable infrastructure for AI agents within hirify.global’s ChipStack SuperAgent ecosystem.
• Build robust evaluation frameworks to measure agent performance, reliability, and alignment with engineering workflows.
• Develop data pipelines, retrieval systems, and context-engineering strategies to support consistent and grounded agent behavior.
• Contribute to continuous integration, automated testing, and observability systems to ensure production-quality deployment of AI-enabled systems.
• Optimize system performance across latency, cost, reliability, and scalability dimensions.

Requirements

• BS with 7+ years of experience OR MS with 5+ years of experience OR PhD with 1+ year of experience.
• Strong software engineering fundamentals, including design, refactoring, debugging, and testing of complex distributed systems. Demonstrated experience building production-quality systems.
• Understanding of large language models (LLMs) and practical considerations for deploying them in real-world systems (latency, cost, reliability, monitoring).
• Experience designing evaluation frameworks for AI systems, including benchmarking, regression testing, and failure analysis.

Nice to have

• Agent architecture: Experience with reason–act loops, planning/evaluation/self-correction patterns, tool/function calling, persistent memory systems, and structured outputs.
• LLM engineering: Familiarity with frontier LLMs and trade-offs across model families; experience with prompt engineering, context management, and alignment techniques.
• Retrieval and data systems: Understanding of RAG pipelines, embeddings, indexing strategies, chunking methodologies, and grounding techniques. Infrastructure and observability: Experience building logging, tracing, monitoring, and evaluation systems for ML/AI applications.
• AI-assisted development workflows: Leveraging AI tools to enhance engineering productivity and code quality.
• Interest in semiconductor design, EDA workflows, and high-performance computing environments.

Culture & Benefits

• Challenge the status quo: Innovators who challenge industry norms and push forward our vision of how silicon should be built.
• Strong opinions, loosely held: Low on ego, but high on collaboration. Okay to be wrong and always open to learning.
• Ship fast, ship quality: Ruthlessly prioritize what matters. Build at lightning speed, but never compromise on the high standards required by the semiconductor industry.
• Proud of our craft: Attention to detail is in our DNA. Take pride in what we build and go the extra mile to ensure an exceptional experience for our users.
• Benefits include: paid vacation and paid holidays, 401(k) plan with employer match, employee stock purchase plan, a variety of medical, dental and vision plan options.