Staff Materials Research Scientist, PFAS Alternative Discovery

ABOUT SANDBOXAQ SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors. We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders. At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact. THE OPPORTUNITY Introduction to the team: The PFAS team sits within SandboxAQ's Chemical Simulation (ChemSim) group. Our mission is to develop PFAS-lean or PFAS-free substitutes and formulations for semiconductor process and fab materials that meet both performance specifications and environmental and safety requirements in complex semiconductor manufacturing environments. We combine generative ML, physics-based simulation (e.g. DFT and molecular dynamics), Large Quantitative Models for property prediction, and multi-scale modeling with a partner-driven experimental validation loop — working alongside industrial co-development partners to move candidate molecules from prediction to qualified use. Introduction to the role: The PFAS team is looking for a Staff Materials Research Scientist to serve as the scientific bridge between our generative chemistry discovery workflow and the external partners who validate our candidate molecules. This role is central to our efforts to ensure that the compounds our AI-driven workflow proposes are directionally correct, appropriate for the target semiconductor use case, and grounded in real manufacturing constraints. This person will: (1) own the partner-facing validation loop — serving as the primary point of contact with our co-development partners on problem definition, target specifications, constraints, and qualification criteria; (2) run our generative chemistry workflow, assess the predicted compounds, and rank them by fitness for the use case to deliver decision-ready shortlists for partner validation; (3) translate experimental feedback from partners into concrete technical improvement points that the rest of the team members can act on; and (4) bring semiconductor domain judgment to bear on the whole pipeline, deciding whether workflow outputs are qualitatively and directionally accurate and validating lead molecules against process reality. See how SandboxAQ is helping build America's semiconductor supply chain from the materials up https://www.sandboxaq.com/post/sandboxaq-helping-build-americas-semiconductor-supply-chain KEY RESPONSIBILITIES - Own the partner validation loop. Serve as the primary scientific point of contact between the PFAS team and external co-development partners (e.g. chemical and process-materials suppliers, semiconductor equipment makers, and control/sensor companies), translating partner problems into well-posed target specifications, constraints, and qualification criteria. - Run the discovery workflow and rank candidates. Operate SandboxAQ's generative chemistry discovery workflow for assigned PFAS-substitution use cases; assess the predicted compounds for chemical plausibility and use-case fit, and rank them to produce decision-ready shortlists that partners can take into experimental validation. - Apply semiconductor domain judgment. Evaluate whether generative and simulation outputs are directionally and qualitatively correct for the target application, and validate lead molecules against real-world semiconductor process, performance, and EHS constraints. - Close the experimental feedback loop. Translate partner validation results and experimental data into specific, actionable technical improvement points for the rest of the team, and follow their incorporation through successive design cycles. - Align targets across internal teams. Partner closely with internal dataset, computational chemistry, machine-learning, and generative-modeling teams to keep property targets, screening oracles, and reward objectives aligned with partner-defined qualification criteria. ESSENTIAL SKILLS & EXPERIENCE - PhD in Chemistry, Chemical Engineering, Materials Science, or a related field, with deep specialization in semiconductor process materials and/or fluorochemistry. - 6+ years of post-PhD experience (or equivalent) in industrial or applied R&D developing, formulating, or qualifying semiconductor process chemical