Staff Materials Research Scientist, PFAS Alternative Discovery

SandboxAQ

10d•$163,200 - $306,000•Hybrid

About The Position

SandboxAQ is seeking a Staff Materials Research Scientist to bridge the gap between their AI-driven chemistry discovery workflow and external partners who validate candidate molecules. This role is crucial for ensuring proposed compounds are suitable for semiconductor manufacturing, meeting performance, environmental, and safety requirements. The scientist will manage partner interactions, run the discovery workflow, assess predicted compounds, translate experimental feedback into technical improvements, and apply semiconductor domain expertise to the entire process. The PFAS team, part of SandboxAQ's Chemical Simulation (ChemSim) group, focuses on developing PFAS-lean or PFAS-free substitutes and formulations for semiconductor process and fab materials. They utilize a combination of generative ML, physics-based simulation, Large Quantitative Models for property prediction, and multi-scale modeling, integrated with partner-driven experimental validation.

Requirements

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 chemicals.
Direct, hands-on experience discovering or evaluating PFAS-lean/PFAS-free alternatives for semiconductor manufacturing and adjacent materials (e.g., wet-etch/clean chemistries, lithography materials, heat-transfer fluids, or data-centre/immersion cooling fluids).
Working knowledge of semiconductor unit processes (e.g., lithography, etch, CMP, cleaning, thermal management) and the performance and EHS specifications that govern process-material qualification.
Demonstrated ability to lead application-driven engagements with external industrial partners.
Demonstrated ability to translate fluently between experimental results and computational/modeling requirements.
Proficiency in Python — sufficient to run and configure computational discovery workflows and interpret their outputs.
Comfort collaborating with generative-ML and physics-based simulation teams.
Familiarity with generative molecular design, high-throughput virtual screening, or ML property prediction for molecules and materials.

Nice To Haves

Direct, hands-on experience with PFAS phase-out or fluorine-free reformulation in a fab or specialty-chemicals setting.
Experience defining qualification protocols or reliability criteria jointly with fabs, OEMs, or chemical suppliers.
Track record of publications or patents in semiconductor materials, fluorochemistry, or PFAS alternatives.
Experience operating within a CHIPS Act or other federally funded R&D program.

Responsibilities

Own the partner validation loop, serving as the primary scientific point of contact with external co-development partners (e.g., chemical and process-materials suppliers, semiconductor equipment makers, and control/sensor companies).
Translate partner problems into well-posed target specifications, constraints, and qualification criteria.
Operate SandboxAQ's generative chemistry discovery workflow for assigned 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.
Translate partner validation results and experimental data into specific, actionable technical improvement points for the team.
Apply semiconductor domain judgment to evaluate the directional and qualitative correctness of generative and simulation outputs for the target application.
Validate lead molecules against real-world semiconductor process, performance, and EHS constraints.
Partner closely with internal dataset, computational chemistry, machine-learning, and generative-modeling teams to ensure alignment of property targets, screening oracles, and reward objectives with partner-defined qualification criteria.