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Senior Application Scientist, Process Chemical Discovery

SandboxAQ

Salary$134K - $252K
Before you apply

Role details

The practical picture
Employment
Full-time
Role signal brief

The role, at a glance

Extracted from the source posting · 95% confidence

This role sits within SandboxAQ's Chemical Simulation group (ChemSim), focusing on developing PFAS-free semiconductor process materials. The Senior Application Scientist will bridge internal AI-driven chemistry discovery and external partner validation, managing the scientific relationship with co-development partners and ensuring proposed compounds meet performance, safety, and manufacturing constraints.

Remote scope
Flexible hybrid work arrangements
Compensation
USD 134,000–252,000 / year

What you need

  • PhD in Chemistry, Chemical Engineering, Materials Science, or a 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 an incumbent in an existing process without losing the properties that process depends on
  • Familiarity with the performance and EHS specifications that govern qualification of process materials
  • Experience bridging experiment and computation on application-driven projects with external partners — turning experimental results into modeling requirements, and model outputs into candidates worth testing
  • 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

What you will own

  • Support the partner validation loop: work with external co-development partners as the day-to-day scientific contact, defining target specifications, constraints, and qualification criteria
  • Run the generative chemistry discovery workflow: operate SandboxAQ's computational discovery workflow for assigned PFAS-substitution use cases, assess compounds for chemical plausibility and use-case fit, and produce decision-ready shortlists
  • Bring domain judgment to the pipeline: assess whether generative and simulation outputs are directionally correct, and validate lead molecules against process, performance, and EHS constraints
  • Close the experimental feedback loop: translate partner validation results and experimental data into specific, actionable technical improvement points for internal teams
  • Align targets across internal teams: partner with dataset, computational chemistry, machine-learning, and generative-modeling teams to keep property targets aligned with partner-defined qualification criteria
Core skills
ChemistryChemical EngineeringMaterials SciencePythonGenerative Molecular DesignHigh-Throughput Virtual ScreeningML Property PredictionComputational ChemistryPartner ManagementProcess Materials Qualification

Remote policyFlexible hybrid work arrangements

Internal freshness window ends Oct 21, 2026
View source evidence
  • summary “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”
  • responsibilities “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”
  • responsibilities “Assess the predicted compounds for chemical plausibility and use-case fit, and rank them to produce decision-ready shortlists”
  • must_have “PhD in Chemistry, Chemical Engineering, Materials Science, or a related field”
  • must_have “3+ years of post-PhD experience (or equivalent) in industrial or applied R&D developing, formulating, or qualifying high-purity performance materials”
  • must_have “Proficiency in Python — sufficient to run and configure computational discovery workflows and interpret their outputs”
  • nice_to_have “Direct, hands-on experience with PFAS phase-out or fluorine-free reformulation in a fab or specialty-chemicals setting”
  • nice_to_have “Working knowledge of semiconductor unit processes (e.g. lithography, etch, CMP, cleaning, thermal management)”
  • salary “$134K - $252K”
  • employment_type “Flexible hybrid work arrangements”
  • company_remote_policy “Flexible hybrid work arrangements, generous PTO, and a culture that respects focus time and recovery”
chemistrychemical engineeringmaterials sciencepython
Role brief

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’ve cultivated 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.

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 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.

See how SandboxAQ is helping build America's semiconductor supply chain from the materials up

Key Responsibilities
  • 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, machine-learning, and generative-modeling teams to keep property targets, screening oracles, and reward objectives aligned with partner-defined qualification criteria.

Essential Skills & Experience
  • PhD in Chemistry, Chemical Engineering, Materials Science, or a 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 an incumbent in an existing process without losing the properties that process depends on.

  • Familiarity with the performance and EHS specifications that govern qualification of process materials.

  • Experience bridging experiment and computation on application-driven projects with external partners — turning experimental results into modeling requirements, and model outputs into candidates worth testing.

  • 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.

Highly Desired Skills & Experience
  • Experience defining qualification protocols or reliability criteria jointly with fabs, OEMs, or chemical supplier

  • Direct, hands-on experience with PFAS phase-out or fluorine-free reformulation in a fab or specialty-chemicals setting.

  • Working knowledge of semiconductor unit processes (e.g. lithography, etch, CMP, cleaning, thermal management).

  • 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.

Why Join Us?

We offer competitive compensation, a comprehensive benefits package, and opportunities for professional growth.

  • Compensation: Competitive base salary commensurate with experience, plus equity and performance-based incentives.

  • Benefits: Comprehensive health, dental, and vision insurance; 401(k) with company match; generous parental leave.

  • Work-Life Balance: Flexible hybrid work arrangements, generous PTO, and a culture that respects focus time and recovery.

  • Career Development: Direct exposure to CHIPS Act-funded programs, senior scientific and executive leadership, mentorship, and dedicated learning budgets to support continued growth.

SandboxAQ Welcomes All

We are committed to fostering a culture of belonging and respect, where diverse perspectives are actively sought and valued. Our multidisciplinary environment provides ample opportunity for continuous growth - working alongside humble, empowered, and ambitious colleagues ready to tackle epic challenges.

Equal Employment Opportunity: All qualified applicants will receive consideration regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status.

Accommodations: We provide reasonable accommodations for individuals with disabilities in job application procedures for open roles. If you need such an accommodation, please let a member of our Recruiting team know.

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