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Entalpic is an AI-driven deep tech company combining machine learning, computational chemistry, and multi-scale physics to discover new materials and optimize manufacturing processes at the atomic scale. The company focuses initially on the semiconductor industry (thin films, ALD, ALE) with applications in catalysis, batteries, and photovoltaics. Backed by $10M in funding, Entalpic works with leading industrial and research partners to connect materials discovery, process optimization, and device performance.
As Application Lead for Vapor Deposition Precursor Chemistry, you will serve as the experimental domain authority on precursors, bridging molecular design, deposition performance, and film properties with the company's computational models. Your core mission is to make the AI platform useful for real-world precursor discovery and atomic-scale manufacturing.
Key responsibilities include:
• Partnerships: Own relationships with external experimental labs and industrial partners; coordinate experimental campaigns; ensure high-quality, timely experimental data delivery.
• Data Quality & Curation: Define gold-standard protocols for experimental data generation; harmonize digital data from various sources (often programmatically); hire, coordinate, and mentor PhD collaborators for data curation; feed observations to technical teams.
• Domain Expertise: Bring deep, practical knowledge of ligand and precursor families, volatility, thermal stability, reactivity with surfaces, and how precursor choice maps to film properties—including tacit knowledge beyond published literature.
• Experimental Strategy: Help define which chemistries and precursor families to explore, screen, and prioritize based on AI insights and experimental validation.
• Growth & Delivery: Support client delivery; bring domain knowledge into product development; contribute to partnership conversations; represent Entalpic at conferences.
This role emphasizes creating a tight learning loop where AI models guide experimental selection and results steer the next iteration. The position can lean toward partnerships or technical data-quality work depending on your strengths. This is NOT a hands-on lab role.
Requirements:
• PhD in chemistry, materials chemistry, chemical engineering, or related field (not required if experience is strong).
• Several years of experience in vapor deposition precursor chemistry: molecular, organometallic, or coordination chemistry for thin-film deposition (ALD/CVD/PVD or closely adjacent field); hands-on experimental or synthetic background at some point in your career.
• Familiarity with software programming (e.g., Python) for analysis of chemistry/materials science characterization data; at minimum, strong willingness to learn.
• Organized, structured, data-minded approach: ability to manage multiple projects, define data processing conventions, and spot subtleties in experimental datasets.
• Ability to communicate outside experimental chemistry culture, translating experimental values and constraints to team members with computer science backgrounds.
• Excellent English communication skills; comfort in client-, partner-, and conference-facing settings.
• Ability to operate independently and drive projects in a fast-paced startup environment.
• Bonus: Exposure to LLMs, agents, data/informatics tooling, electronic lab notebooks, databases; existing network across precursor suppliers, equipment providers, fabs, and research institutes.
