Staff Materials Research Scientist, PFAS Alternative Discovery

Entangled Future; United States; Remoto; USD 180.000 - 240.000; Tempo pieno

Descrizione del lavoro

SandboxAQ is seeking a Staff Materials Research Scientist in the PFAS Alternative Discovery area within the ChemSim group. You will serve as the scientific bridge between our generative chemistry discovery workflow and external partners validating candidate molecules for semiconductor applications, ensuring outputs meet process and safety constraints.

You will own the partner validation loop, run the discovery workflow, rank compounds for experimental validation, and translate feedback into

Competenze

• PhD in a chemistry, chemical engineering, or materials science field with semiconductor process materials focus.
• Experience leading application-driven engagements with external industrial partners.
• Ability to translate experimental results to computational/modeling requirements.
• Familiarity with generative chemistry, high-throughput virtual screening, or ML property prediction for molecules and materials.

Mansioni

• Own the partner validation loop as the primary scientific contact with co-development partners.
• Run SandboxAQ's generative chemistry discovery workflow; assess compounds for plausibility and fit; rank for experimental validation.
• Apply semiconductor domain judgment to ensure outputs are directionally correct and validate lead molecules against process constraints.
• Translate partner validation results into concrete improvement points for the team and ensure incorporation through design cycles.
• Collaborate with internal teams to align property targets and qualification criteria with partner needs.

Conoscenze

External partnerships

Python

Generative chemistry

High-throughput screening

Molecular design

Formazione

PhD in Chemistry, Chemical Engineering, Materials Science

Descrizione del lavoro

Staff Materials Research Scientist, PFAS Alternative Discovery at SandboxAQ, United States (remote). A lead-level full time position in the ChemSim team, listed under research.

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.

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.

Requirements

• PhD in Chemistry, Chemical Engineering, Materials Science, or a related field, with deep specialization in semiconductor process materials and/or fluorochemistry.
• 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 and 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 — and 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.
• 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.