Founding Staff AI Engineer — Physics AI, Agentic Systems & Full-Stack

About Elucida Labs

Elucida Labs is building the autonomous AI Scientist for Semiconductor Manufacturing .

Advanced wafer fabrication, spanning thin-film deposition (ALD, CVD, PVD), plasma etch (RIE, ALE), CMP, and advanced packaging, is bottlenecked by brutal multi-month empirical trial-and-error cycles. We build intelligent software that eliminates months of guesswork and trial-and-error in semiconductor manufacturing.

Backed by top founders and mentors (Stanford StartX F26), we work with semiconductor equipment OEMs, microelectronics research institutes, and Fabs. As a Founding Staff AI Engineer, you will own the technical architecture from core scientific models to live customer deployments.

The Role

We are seeking an exceptional, high-agency Founding Staff AI Engineer who thrives at the intersection of deep scientific modeling, autonomous agentic architectures, and rapid production full-stack engineering.

We operate with extreme engineering leverage: our team heavily uses agentic developer tooling, including Claude Code, Cursor, and the Model Context Protocol (MCP), to ship robust, production-grade systems at 10x startup velocity. You will bridge physical process models and large-scale autonomous LLM agents, write full-stack code, and deploy directly into customer evaluations and pilots.

Key Responsibilities

1. Physics AI & Scientific Machine Learning

• Design and implement Physics-Informed Neural Networks (PINNs), neural operators (FNOs, DeepONets), and surrogate models to simulate plasma dynamics, chemical kinetics, thermal budgets, and material interfaces.
• Build active learning and Bayesian optimization pipelines to recommend optimal recipe parameters (gas flow, RF power, pressure, temperature profiles) under sparse, high-cost experimental constraints.
• Ingest and featurize complex, multimodal semiconductor fab telemetry: optical emission spectroscopy (OES), residual gas analysis (RGA), in-situ metrology, and wafer-level morphology data.

2. Agentic AI & Autonomous Scientific Workflows

• Develop robust, deterministic multi-agent systems that autonomously decompose complex engineering goals into actionable experimental design, data querying, and simulation workflows.
• Build tool-use capabilities and MCP (Model Context Protocol) servers allowing agents to execute scientific code, run numerical solvers, and synthesize technical papers/patents.
• Implement structured reasoning, guardrails, state-machine verifiers, and memory stores to ensure agent outputs meet rigorous physical plausibility and safety criteria.

3. Full-Stack Engineering & AI-Augmented Velocity

• Architect, build, and maintain our end-to-end software platform: high-performance backend microservices (Python/FastAPI/AsyncIO), database layers, and intuitive engineering dashboards (TypeScript/React/Next.js).
• Leverage agentic coding tools (such as Claude Code CLI and modern agent environments) to autonomously write test suites, refactor codebases, automate CI/CD tasks, and maximize raw development output.
• Construct robust ETL pipelines and secure data connectors capable of ingesting high-frequency equipment time-series and legacy SECS/GEM or flat-file fab logs.

4. Customer Delivery & Pilot Execution

• Own technical deployment and integration during pilot programs with equipment OEMs and cleanroom customer sites.
• Work shoulder-to-shoulder with customer process engineers, CTOs, and fab managers to translate complex domain challenges into working product capabilities.
• Deploy air-gapped or hybrid on-prem/VPC customer instances ensuring enterprise-grade data isolation, encryption, and zero-data-leakage compliance.

Qualifications

• Scientific & Applied ML Background: Proven experience building and deploying scientific ML models, PINNs, surrogate models, or complex physical simulation workflows (fluids, plasma, materials, or chemical engineering domains).
• Agentic & LLM Expertise: Hands-on experience building production-grade agentic frameworks (LangGraph, AutoGen, CrewAI, or bespoke state machines), advanced tool-calling, evaluation frameworks, and retrieval-augmented generation (RAG).
• AI-First Developer Leverage: Daily fluency with agentic coding environments (especially Claude Code, Cursor, or custom terminal agents) and protocols like MCP to accelerate prototyping, multi-file refactors, and test coverage.
• Full-Stack Competence: Proficiency with modern backend and frontend stacks (Python/FastAPI, TypeScript/React/Next.js, Node.js, Tailwind, GraphQL/REST).
• Data & Systems Infrastructure: Experience with relational and time-series databases (PostgreSQL, TimescaleDB, ClickHouse), vector databases, Docker, Kubernetes, and cloud infrastructure (AWS/GCP/Azure).
• Startup Velocity & Customer Focus: Ability to navigate ambiguity, ship working code daily, iterate directly based on customer feedback, and take extreme end-to-end ownership.
• U.S. Person : Due to US export control regulations, this position strictly requires the applicant to be a 'U.S. Person' (a U.S. Citizen, U.S. Permanent Resident, or approved Asylee/Refugee).

Bonus Points

·       Advanced Degree: Ph.D. from a Tier-1 university (e.g., Stanford, MIT, UC Berkeley, CMU, Caltech, UIUC, or top international equivalent) in Computer Science, Applied Physics, Materials Science, Chemical Engineering, Electrical Engineering, or related technical disciplines.

·       Core Compute/Systems: Experience with high-performance computing and physics engines (C++).

·       AI & Physics Modeling: Familiarity with physics-informed machine learning and AI frameworks (NVIDIA Modulus, NVIDIA NeMo, PyTorch).

·       Semiconductor Domain Knowledge: Prior background or academic research in semiconductor processing, nanotechnology, thin films (ALD, CVD, Etch, CMP), or electronic materials.

·       Fab Protocols & Tool Interfacing: Experience interfacing with hardware control systems, industrial IoT, or fab communication protocols (SECS/GEM, EDA/Interface A).

·       Tooling & Protocol Extensions: Experience authoring custom MCP servers or building integrations for agentic coding tools.

·       Early-Stage Experience: Previous experience as a founder, founding engineer, or early-stage engineer in a venture-backed deep-tech or enterprise AI startup.

Why Join Elucida Labs?

• Pivotal Ownership: Ground-floor equity and outsized influence in setting company architecture, culture, and technical vision.
• Massive Industrial Impact: Directly accelerate the physical limits of semiconductor fabrication, advanced packaging, and microelectronics manufacturing.
• World-Class Ecosystem: Tight access to top-tier semiconductor industry executives, fab leaders, and the Stanford StartX network.