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SandboxAQ is a high-growth AI company spun out from Alphabet in 2022, developing Large Quantitative Models (LQMs) to address challenges in life sciences, financial services, navigation, cybersecurity, and other sectors. The company combines generative ML, physics-based simulation, and multi-scale modeling with experimental validation loops.
The PFAS team within the Chemical Simulation (ChemSim) group is developing PFAS-lean or PFAS-free substitutes for semiconductor process and fab materials. This role bridges SandboxAQ's generative chemistry discovery workflow and external industrial partners who validate candidate molecules.
Key responsibilities:
- Serve as day-to-day scientific contact with co-development partners (chemical suppliers, semiconductor equipment makers, control/sensor companies), translating their problems into well-posed target specifications, constraints, and qualification criteria.
- Operate SandboxAQ's generative chemistry discovery workflow for PFAS-substitution use cases; assess predicted compounds for chemical plausibility and use-case fit, and rank them to produce decision-ready shortlists for partner validation.
- Assess whether generative and simulation outputs are directionally and qualitatively correct for target semiconductor applications, and validate lead molecules against process, performance, and EHS constraints.
- Translate partner validation results and experimental data into specific, actionable technical improvement points for internal teams, and track their incorporation through design cycles.
- 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.
The role requires bridging experiment and computation on application-driven projects, turning experimental results into modeling requirements and model outputs into testable candidates. You will work within a global, multidisciplinary team of AI, chemistry, physics, mathematics, and engineering experts.
REQUIREMENTS:
- PhD in Chemistry, Chemical Engineering, Materials Science, or related field.
- 3+ years of post-PhD experience (or equivalent) in industrial or applied R&D developing, formulating, or qualifying high-purity performance materials and formulations to replace incumbents in existing processes without losing critical properties.
- Familiarity with performance and EHS specifications governing qualification of process materials.
- Experience bridging experiment and computation on application-driven projects with external partners.
- Proficiency in Python sufficient to run and configure computational discovery workflows and interpret outputs.
- Familiarity with generative molecular design, high-throughput virtual screening, or ML property prediction for molecules and materials.
HIGHLY DESIRED:
- Experience defining qualification protocols or reliability criteria with fabs, OEMs, or chemical suppliers.
- Direct hands-on experience with PFAS phase-out or fluorine-free reformulation in fab or specialty-chemicals settings.
- Working knowledge of semiconductor unit processes (lithography, etch, CMP, cleaning, thermal management).
- Track record of publications or patents in semiconductor materials, fluorochemistry, or PFAS alternatives.
- Experience operating within CHIPS Act or other federally funded R&D programs.
