Founding Staff Full Stack Engineer

Backed by top founders and mentors (including Stanford StartX & IIT Startups), we work with semiconductor equipment OEMs, microelectronics research institutes, and Fabs. As a Founding Staff Full Stack Engineer, you will own the technical architecture from core scientific models to live customer deployments., We are seeking an exceptional, high-agency Founding Staff Full-Stack Engineer who thrives at the intersection of rapid production engineering, scalable software architecture, and autonomous agentic systems.

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 be the architectural bedrock of our product, building the end-to-end software platform and cloud infrastructure that bridges our physical process models and large-scale autonomous LLM agents, and deploying directly into customer evaluations and pilots., 1. Full-Stack Engineering & Platform Architecture

• Architect, build, and maintain our end-to-end software platform from scratch: high-performance backend microservices (Python/FastAPI/AsyncIO), complex database layers, and intuitive engineering dashboards (TypeScript/React/Next.js).
• Design and construct robust ETL pipelines and secure data connectors capable of ingesting high-frequency equipment time-series telemetry (OES, RGA) and legacy SECS/GEM or flat-file fab logs.

1. Cloud Infrastructure, Deployment & Continuous Testing (DevOps) * Design, provision, and manage scalable cloud infrastructure across AWS, GCP, or Azure using Infrastructure as Code (Terraform, Pulumi). * Build and maintain rigorous CI/CD pipelines with comprehensive continuous testing (automated unit, integration, and end-to-end testing) to ensure seamless, zero-downtime deployments and highly reliable iteration cycles. * Orchestrate and scale containerized workloads using Docker and Kubernetes, optimizing compute resources for heavy ML, physics simulations, and LLM inference tasks.

2. AI-Augmented Velocity & Agentic Tooling * Leverage agentic coding tools (such as Claude Code CLI and Cursor) to autonomously generate test coverage, refactor codebases, automate deployment tasks, and maximize raw development output. * Build tool-use capabilities and MCP (Model Context Protocol) servers allowing our core AI agents to execute scientific code, query databases, and run numerical solvers. * Collaborate closely with the AI and Physics teams to seamlessly integrate LLM agents, LangGraph workflows, and Physics-Informed Neural Networks (PINNs) into the production backend.

3. 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.

• Full-Stack Competence: 5+ years of production experience with modern backend and frontend stacks (Python/FastAPI, TypeScript/React/Next.js, Node.js, Tailwind, GraphQL/REST).
• Data & Systems Infrastructure: Deep experience designing schemas and managing relational, time-series, and vector databases (PostgreSQL, TimescaleDB, ClickHouse, Pinecone/Milvus).
• Cloud & DevOps Expertise: Deep hands-on experience provisioning cloud infrastructure (AWS/GCP/Azure), building CI/CD pipelines, writing Infrastructure as Code (Terraform), and managing Kubernetes/Docker clusters.
• 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.
• Applied LLM/Agentic Exposure: Hands-on experience integrating LLM APIs, building RAG pipelines, or deploying agentic frameworks (LangGraph, AutoGen) into production software.
• 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.

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).

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.

• 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.