opportunities.lamresearch.com
Software Engineer Sys 5
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 7+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 7+ years of shipping production software, with a strong track record of owning features end-to-end
- Proven experience building AI agents, LLM-driven systems, or autonomous workflows with proper guardrails and evaluation mechanisms.
- Have practical AI systems experience with RAG, evals, monitoring, MCP/tool use, structured outputs, or multi-agent workflows
- Proven experience with LLM orchestration patterns and frameworks (LangGraph, MCP, agent SDKs from major model providers).
- Foundational AI skills, including prompt engineering, model evaluation, agent orchestration, reasoning frameworks, and the ability to apply AI to improve engineering and operational outcomes.
- Familiarity with context management, session state, or memory systems in AI or distributed systems. You’ve thought about what the model sees and why it matters.
- A systems-thinking mindset that connects AI models, data pipelines, and operational workflows end-to-end.
- Strong communication skills with the ability to influence senior engineers and product leaders.
- A passion for how AI can unlock potential to help our teams, our customers, and our communities achieve great things.
Nice to have
- Prior semiconductor equipment experience.
- Familiarity with human-in-the-loop workflows, PEFT based fine-tuning, and LLM evaluation techniques.
- Familiarity with AI safety, guardrails, monitoring, and responsible deployment practices
- Proficiency with AI tracing and evaluation tools such as LangSmith to monitor hallucinations, latency, and tokenomics.
- Understanding of reinforcement learning, including RLHF, RLAIF, offline and online RL, safety-aware reward design, and feedback loops.
- Expertise in AI evaluation platforms, synthetic data generation, model-based judges, or red-teaming.
- Experience building and shipping multi-model AI systems in production.
- Experience operating in regulated environments. Solid grasp of security fundamentals - encryption, access controls, audit logging, secrets management.
Full description
The group you’ll be a part of
In the Global Products Group, we are dedicated to excellence in the design and engineering of Lam's etch and deposition products. We drive innovation to ensure our cutting-edge solutions are helping to solve the biggest challenges in the semiconductor industry.
The impact you’ll make
As a Software Engineer at Lam, you will be at the forefront of innovation by designing, developing, and troubleshooting, and debugging software programs. Your role is pivotal in developing software tools that support design, infrastructure, and technology platforms. Your expertise will determine hardware compatibility and influence design, ensuring seamless integration between software and hardware.
In this role, you’ll make an impact across Lam’s entire product portfolio of equipment working within our centralized software engineering team, collaborating with some of the brightest minds in the industry.
What you’ll do
- Design and implement sophisticated architectural solutions for integrating LLM technology into business applications
- Develop complex multi-step agentic pipelines around open- and closed-source LLMs.
- Own infrastructure for model integration, routing, and evaluation.
- Build MCP Servers - ensure model governance, versioning, access control, and monitoring for production AI systems.
- Evaluate, prototype, and integrate the latest advancements in AI and agentic systems as they emerge.
- Collaborate closely with cross-functional teams including business leaders, engineers, architects, and designers to align AI systems with business objectives.
Who we’re looking forMinimum Qualifications:
- 7+ years of shipping production software, with a strong track record of owning features end-to-end
- Proven experience building AI agents, LLM-driven systems, or autonomous workflows with proper guardrails and evaluation mechanisms.
- Have practical AI systems experience with RAG, evals, monitoring, MCP/tool use, structured outputs, or multi-agent workflows
- Proven experience with LLM orchestration patterns and frameworks (LangGraph, MCP, agent SDKs from major model providers).
- Foundational AI skills, including prompt engineering, model evaluation, agent orchestration, reasoning frameworks, and the ability to apply AI to improve engineering and operational outcomes.
- Familiarity with context management, session state, or memory systems in AI or distributed systems. You’ve thought about what the model sees and why it matters.
- A systems-thinking mindset that connects AI models, data pipelines, and operational workflows end-to-end.