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Posted on 2025/12/06

Battery Scientist

DeepRec.ai

Boston, MA, United States

Full-time

Qualifications

  • PhD in Materials Science, Chemical Engineering, Electrochemistry, or related field
  • Strong R&D leadership experience directing multi-disciplinary teams
  • Deep expertise in electrolyte chemistry, interfacial science, SEI engineering, and cell-level validation
  • Experience structuring experimental chemistry data for AI applications

Responsibilities

  • Lead multi-disciplinary R&D teams and programs on next-gen battery electrolytes
  • Direct SEI engineering, additive optimization, and cell-level validation
  • Ensure data-driven experimentation with reproducible, high-quality results for AI model integration
  • Define materials innovation roadmap in electrolytes and interfacial science

Full Description

Role: Battery Electrolyte Scientist / Materials Chemistry Lead

Location: Flexible / Hybrid Possible

Type: Full-time

About the Role:

Join a cutting-edge AI + materials R&D team driving next-gen battery technology.

Lead electrolyte and interfacial chemistry programs, produce high-quality experimental data, and guide AI-driven materials innovation.

Key Responsibilities:

• Lead multi-disciplinaryR&D teams and programs on next-gen battery electrolytes.

• Direct SEI engineering, additive optimization, and cell-level validation.

• Ensure data-driven experimentation with reproducible, high-quality results for AI model integration.

• Define materials innovation roadmap in electrolytes and interfacial science.

Requirements:

• PhD in Materials Science, Chemical Engineering, Electrochemistry, or related field.

• Strong R&D leadership experience directing multi-disciplinary teams.

• Deep expertise in electrolyte chemistry, interfacial science, SEI engineering, and cell-level validation.

• Experience structuring experimental chemistry data for AI applications.

Preferred:

• Experience at top battery manufacturers or applied materials chemistry labs.

• Proficiency with molecular informatics tools for materials optimization.

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