{"id":474387,"url":"https://alion.io/job/ses-ai-computational-materials-scientist","title":"Computational Materials Scientist","company":{"id":31799,"name":"SES AI","domain":"ses.ai","url":"https://alion.io/company/ses","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"D","score":42,"open_postings":35,"ghost_share":0.971,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Science & Research","role_family":"Science & Research","seniority":null,"employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Boston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":180000,"max":200000,"currency":"USD","period":"year","gross":null,"usd_annual":200000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Pandas","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"TensorFlow","optional":false},{"name":"MATLAB","optional":true},{"name":"SQL","optional":true},{"name":"Time Series Forecasting","optional":true}],"status":"live","first_seen_at":"2025-12-03T10:04:16Z","employer_posted_date":"2026-06-22","last_verified_at":"2026-10-18T08:50:51Z","board_verified":true,"closed_at":null,"days_open":319,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":318},"description":"SES AI Corp. (NYSE: SES) is dedicated to accelerating the world’s energy transition through groundbreaking material discovery and advanced battery management. We are at the forefront of revolutionizing battery creation, pioneering the integration of cutting-edge machine learning into our research and development. Our AI-enhanced, high-energy-density and high-power-density Li-Metal and Li-ion batteries are unique; they are the first in the world to utilize electrolyte materials discovered by AI. This powerful combination of \"AI for science\" and material engineering enables batteries that can be used across various applications, including transportation (land and air), energy storage, robotics, and drones. \n To learn more about us, please visit: www.ses.ai \nWhat We Offer: \nA highly competitive salary and robust benefits package, including comprehensive health coverage and an attractive equity/stock options program within our NYSE-listed company. \nThe opportunity to contribute directly to a meaningful scientific project-accelerating the global energy transition-with a clear and broad public impact. \nWork in a dynamic, collaborative, and innovative environment at the intersection of AI and material science, driving the next generation of battery technology. \nSignificant opportunities for professional growth and career development as you work alongside leading experts in AI, R&D, and engineering. \nAccess to state-of-the-art facilities and proprietary technologies are used to discover and deploy AI-enhanced battery solutions. \nWhat we Need: \nThe SES AIPrometheus team is seeking an exceptional Computational Materials Scientist to combine physics-based simulation (DFT, MD, quantum modeling) with AI-assisted material prediction to generate high-quality training data and accelerate materials discovery. This role is crucial for advancing our understanding of electrochemical energy materials at the atomic level.As a Computational Materials Scientist, you will be a core data-driven modeler responsible for executing and automating complex simulations. \nEssential Duties and Responsibilities: \n Atomistic Modeling & Simulation \nConduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum Mechanics) simulations of battery components, including electrolytes, coatings, and electrodes. \nDevelop and refine ML-enhanced force fields and surrogate models to accelerate simulation time scales and enable multi-scale simulation efforts. \nApply expertise in atomistic simulation and quantum modeling to solve key challenges in electrochemical energy materials (e.g., batteries/fuel cells). \n\nAI Data Generation & Prediction \nGenerate high-quality, structured simulation data to serve as training sets for AI property prediction models and material screening modules. \nContribute to the development of battery domain LLM features and advanced property-prediction models. \nAutomate complex simulation workflows using strong coding practices to enhance efficiency and scalability. \n\n Collaboration & Tooling \nCollaborate with experimental teams, leveraging a hybrid computational + experimental literacy to validate models and drive design iteration. \nUtilize advanced simulation tools (VASP, Quantum Espresso) and data science libraries (TensorFlow, Pandas) to manage and analyze large datasets. \n\nEducation and/or Experience: \nEducation: Ph.D. in Mechanical Engineering, Materials Science, Chemical Engineering, or a closely related computational/physics field. \nCore Simulation Expertise: Deep and extensive experience in atomistic simulation and quantum modeling, including proficiency with key QM/DFT tools (VASP, Quantum Espresso) and MD simulations. \nDomain Focus: Strong background in electrochemical energy materials and extensive computational work focused on batteries/fuel cells. \nCoding Proficiency: Strong coding skills in Python (along with related libraries like Pandas and TensorFlow) for simulation workflow automation and data analysis. \nML Application: Experience in developing or utilizing ML-enhanced force fields and surrogate models for materials prediction., or equivalent practical experience. \nPreferred Qualifications: \nLLM Development: Experience in developing battery domain LLM features or property-prediction models. \nHybrid Skillset: Demonstrated experience working in a hybrid computational + experimental environment. \nTooling Diversity: Familiarity with additional data analysis tools like R, SQL, MATLAB, and time-series forecasting libraries like Prophet. \nTarget Background: Previous experience at national laboratories, XtalPi, Entalpic, or deep battery modeling groups. \nThe salary range for this position as required under applicable pay transparency laws.\nSalary Range\n$180,000—$200,000 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