Senior Application Scientist, Process Chemical Discovery

🕒 vor 1 Monat

🇺🇸 Vereinigte Staaten – Remote

💵 $134.000 - $252.000 / Jahr

⏰ Vollzeit

🟠 Senior

👻 Geisterscore 0%

Was es bedeutet: die Wahrscheinlichkeit, dass für diese Stelle niemand wirklich einstellt.So liest du es: 60 % oder höher heißt, wir halten das für einen Geisterjob. Niedrigere Werte sind weniger bedenklich.Warum dieser Wert:

• Seit 43 Tagen offen.
• Stellen bei diesem Unternehmen werden meist in 64 Tagen besetzt.

Jetzt Bewerben

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SandboxAQ

WebsiteLinkedInAlle Stellen

51 - 200 Mitarbeiter

Gegründet 2021

🤖 Künstliche Intelligenz

🔒 Cybersecurity

💊 Pharmazie

Artificial Intelligence • Cybersecurity • Pharmaceuticals

SandboxAQ ist ein Unternehmen, das quantitative KI- und quanteninspirierte Technologien entwickelt, um reale Probleme in den Bereichen Arzneimittelforschung, Materialwissenschaft, Cybersicherheit und Navigation zu lösen. Sie erstellen große quantitative Modelle (LQMs), die auf physikalischen und chemischen Grundlagen beruhen, um molekulare Eigenschaften vorherzusagen, therapeutische Designs zu beschleunigen und komplexe chemische und katalytische Prozesse zu simulieren, während sie auch ihr Fachwissen in KI und Post-Quanten-Kryptografie einsetzen, um digitale Sicherheit und Navigation in GPS-losen Umgebungen zu verbessern.

Beschreibung

• Serve as the scientific bridge between the generative chemistry discovery workflow and external partners validating candidate molecules • Work with co-development partners as a day-to-day scientific contact on problem definition, target specifications, constraints, and qualification criteria • Operate the generative chemistry discovery workflow for PFAS-substitution use cases • Assess predicted compounds for chemical plausibility and use-case fit • Rank candidates and produce decision-ready shortlists for partner experimental validation • Assess generative and simulation outputs against semiconductor process, performance, and EHS constraints • Translate partner validation results and experimental data into actionable technical improvements • Follow technical improvements through successive design cycles • Collaborate with dataset, computational chemistry, machine-learning, and generative-modeling teams • Align property targets, screening oracles, and reward objectives with partner qualification criteria

🎯 Anforderungen

• 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 required properties • Familiarity with performance and EHS specifications governing process-material qualification • Experience bridging experiment and computation on application-driven projects with external partners • Proficiency in Python sufficient to run and configure computational discovery workflows and interpret 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 • Experience defining qualification protocols or reliability criteria jointly with fabs, OEMs, or chemical suppliers • 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, including lithography, etch, CMP, cleaning, or 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 • Candidates cannot participate in foreign talent programs sponsored by China, Russia, Iran, or North Korea • Must be legally authorized to work in the country where the job is located

🏖️ Vorteile

• Equity and performance-based incentives • Comprehensive health, dental, and vision insurance • 401(k) with company match • Generous parental leave • Flexible hybrid work arrangements • Generous PTO • Direct exposure to CHIPS Act-funded programs • Mentorship • Dedicated learning budgets • Professional growth opportunities

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