Onto Innovation is looking to build practical AI helpers to speed up tasks related to recipe setup, troubleshooting, fleet management analytics, and expert guidance from internal knowledge for their inspection & metrology tools used by semiconductor device manufacturers.
Requirements
- Python proficiency (data wrangling, APIs, packaging); comfort on Linux and with Git.
- Built at least one LLM app using a framework such as LangChain, LlamaIndex, or Semantic Kernel.
- Hands-on with vector search (e.g., FAISS/Weaviate/Milvus) and embeddings; understands chunking, metadata, and hybrid search basics.
- Familiarity with RAG and prompt engineering; can measure quality (groundedness/relevance) and reduce hallucinations.
- Basic backend skills (REST/JSON, auth, environment secrets); experience containerizing with Docker.
- Worked with agent frameworks (LangGraph, AutoGen, CrewAI) or implemented tool-calling/plan-execute loops.
- Experience parsing complex docs (e.g., Unstructured, GROBID) and handling images/figures from manuals.
Responsibilities
- Prototype AI assistants & agents for field workflows: guided recipe setup, log triage, playbook lookups, parts/alarms advice, and fleet-wide health checks.
- Build retrieval systems (RAG): ingest manuals, specs, ticket notes, recipes, logs, and best-practice docs; design chunking, embeddings, and indexing; tune prompts and retrieval for accuracy/latency.
- Connect AI to our tools and data: stand up MCP servers (Model Context Protocol) and other connectors to safely expose internal systems (document stores, MES, issue trackers, telemetry APIs) to LLMs.
- Fine-tune or adapt models (e.g., LoRA/QLoRA) for domain terms, error codes, and tool-specific intents when retrieval alone isn’t enough.
- Evaluate and harden: set up offline & online evals for groundedness/relevance; add guardrails, observability, and traceability; write runbooks.
- Ship small apps: package prototypes behind simple APIs or lightweight UIs that field engineers can use (web chat, Slack/Teams bots, or CLI).
- Data plumbing: parse messy PDFs/images/CSVs; normalize schemas for recipes, events, alarms, SPC/trace data.
Other
- Partner closely with our AI Lead Engineer and collaborate with field/service engineers who support our inspection & metrology tools across fabs.
- Comfortable reading technical manuals/logs and collaborating with non-software teammates.
- Pragmatic, security-minded, iterate-in-the-open with our engineers.
- We value curiosity, clear writing, and the grit to trace weird edge cases in logs and manuals.
- Send your resume/GitHub/portfolio and a short note about an LLM or agent project you’ve built (what made it work, what you measured, and what you’d improve).
