Senior Application Scientist, Process Chemical Discovery SandboxAQ in US National

Location:

United States

Employment Type:

Full time

Location Type:
Remote

Department: AI Simulation Chem Sim

US Tier 1 Senior $168K - $252K

US Tier 2 Senior $151K - $227K

US Tier 3 Senior $134K - $202K

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’re cultivating 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.

Introduction to the team:
The PFAS team sits within SandboxAQ's Chemical Simulation (Chem Sim) 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 Senior 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) support the partner-facing validation loop - working with our co-development partners as a day-to-day scientific contact 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 domain judgment to bear on the whole pipeline, assessing whether workflow outputs are qualitatively and directionally accurate and validating lead molecules against process reality.

• Support the partner validation loop. Work with external co-development partners (e.g. chemical and process-materials suppliers, semiconductor equipment makers, and control/sensor companies) as a day-to-day scientific contact, helping turn 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.
• Bring domain judgment to the pipeline. Assess whether generative and simulation outputs are directionally and qualitatively correct for the target semiconductor application, and check lead molecules against the 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,…