# Staff Materials Research Scientist, PFAS Alternative Discovery at SandboxAQ

- Company: SandboxAQ
- What the company does: SandboxAQ leverages the compound effects of AI and advanced computing to address some of the biggest challenges impacting society. SandboxAQ technologies include AI simulation, cryptography management for cybersecurity, and AI sensing for global organizations.
- Company website: https://www.sandboxaq.com
- Type: Startups (AI role)
- Level: Senior
- Location: United States
- Work setup: Remote
- Pay: $163K to $306K base salary per year (USD)
- Posted: 2026-07-24
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/sandboxaq/0e90492a-f46b-45d0-8d5d-5a96143fb193
- Page: https://www.1752.vc/careers/jobs/sandboxaq-staff-materials-research-scientist-pfas-alternative-discovery/

## About the role

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.

## What they're looking for

- 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

Tags: AI Simulation