Software Engineer 2 at Onto Innovation in Wilmington, MA

Summary: Onto Innovation is a leader in process control and advanced semiconductor technologies. They are seeking a Junior AI Engineer to build practical AI helpers that enhance workflows in inspection and metrology tools, collaborating closely with field engineers and the AI Lead Engineer. 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 - Computer Vision – understanding, defect detection, segmentation, or SEM/optical imaging - Work like an engineer: write readable Python/TypeScript, tests, and docs; use Git; participate in code reviews; iterate fast with the AI lead and domain SMEs Required Qualifications: - BS in CS/EE/CE/ME (or equivalent experience) - 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 - Comfortable reading technical manuals/logs and collaborating with non-software teammates Preferred Qualifications: - Worked with agent frameworks (LangGraph, AutoGen, CrewAI) or implemented tool-calling/plan-execute loops - Built or configured MCP servers to connect LLMs to internal data/tools - Experience parsing complex docs (e.g., Unstructured, GROBID) and handling images/figures from manuals - Exposure to semiconductor equipment or factory systems (SECS/GEM, EDA/Interface A, MES, SPC); familiarity with KLA/AMAT/TEL/ASML tool ecosystems - Time-series and log analysis (Pandas, SQL, TimescaleDB/InfluxDB), wafer map/vision background, or simple CV - Model adaptation experience (LoRA/QLoRA, PEFT) and experiment tracking (MLflow/W&B) - LLM observability/evals (Ragas, TruLens, LangSmith), basic security/PII handling, and role-based access - Cloud familiarity (AWS/Azure/GCP) and lightweight front-ends (React/Next.js) for internal tools - Prior work on fleet-level dashboards/analytics or recipe/parameter management Required Skills: Python proficiency, LLM app development, Vector search experience, Backend skills Important Skills: Containerization with Docker, Technical manual reading Nice-to-Have Skills: Curiosity, Clear writing, Collaboration with teammates Benefits: Health, dental, and vision coverage, Life and disability insurance, PTO, 401(k) with employer match, Employee Stock Purchase Program (ESPP), Wellness initiatives