SES AI
Product Manager – AI/ML Computing Infrastructure & Simulation Platform
SES AI, Boston, Massachusetts, us, 02298
Product Manager – AI/ML Computing Infrastructure & Simulation Platform
6 days ago Be among the first 25 applicants
SES AI Corp. (NYSE: SES) is dedicated to accelerating the world’s energy transition through groundbreaking material discovery and advanced battery management. We are pioneering the integration of cutting‑edge machine learning into battery R&D, and our AI‑enhanced, high‑energy‑density and high‑power‑density Li‑Metal and Li‑ion batteries 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 for transportation (land and air), energy storage, robotics, and drones—truly taking us Beyond Li‑ion™.
To learn more, visit: www.ses.ai
What We Offer
Highly competitive salary and robust benefits, including comprehensive health coverage and attractive equity/stock options in a NYSE‑listed company.
The opportunity to contribute directly to a meaningful scientific mission: accelerating the global energy transition with clear and broad public impact.
A dynamic, collaborative environment at the intersection of AI, computational science, and advanced battery technology.
Significant opportunities for growth as you collaborate with leading experts in AI, R&D, and engineering.
Role Overview We are seeking a Product Manager for AI/ML Computing Infrastructure & Simulation to own the roadmap for SES AI’s scientific computing platform, ML toolchain, and simulation workflows.
This role sits at the intersection of machine learning, computational chemistry, and platform engineering, guiding how automated quantum simulations, real‑time AI inference, and large‑scale molecular workflows are productized and delivered as reliable, usable systems for scientists and engineers.
You will partner closely with AI researchers, computational chemists, software engineers, and business stakeholders to turn cutting‑edge research—such as automated DFT pipelines and generative models for molecular and battery materials—into scalable, production‑grade products aligned with SES’s strategic roadmap.
Key Responsibilities
Own the product vision and roadmap for the AI/ML computing and simulation platform, spanning quantum simulations, molecular modeling, and AI‑assisted workflows.
Translate scientific and R&D requirements into clear platform capabilities, milestones, and prioritized backlogs.
Align research outputs with platform usability, reliability, and business impact, ensuring delivery against roadmap commitments.
Infrastructure, Toolchain & Workflows
Define requirements for automated quantum simulations (e.g., automated DFT workflows for battery materials) and their integration into the core platform.
Shape the architecture and user experience of real‑time AI inference systems serving molecular and battery models.
Collaborate with engineering leads on CI/CD pipelines, data streaming, and orchestration for simulation and AI workflows.
Guide integration of tools across the ML stack (training, evaluation, deployment, monitoring) into cohesive, data‑driven workflows.
Work with ML researchers to design and productize neural networks and generative AI models trained on molecular and battery datasets.
Define requirements for AI evaluation, benchmarking, and monitoring, including performance, robustness, and scientific relevance.
Incorporate domain-level understanding of molecular physics and computational chemistry into AI workflows to ensure scientific validity.
User Experience & Stakeholder Engagement
Serve as the connective tissue between computational chemists, platform engineers, AI researchers, and product leadership.
Gather and synthesize user feedback from scientists and engineers to continuously refine platform features, APIs, and UX.
Communicate roadmap, trade‑offs, and release plans to technical and non‑technical stakeholders.
Governance & Performance
Define and track KPIs for platform usage, computational scalability, AI engine performance, and time‑to‑insight for R&D teams.
Drive best practices for reliability, observability, and performance optimization of AI and simulation engines.
Core Competencies
Automated quantum simulations (e.g., high‑throughput, scripted DFT or ab initio workflows)
Real‑time AI inference and serving of ML models in production environments
Computational scalability and performance optimization of AI engines and simulation workloads
CI/CD for ML and scientific computing, including automated testing and deployment
Data streaming and data pipeline design for large‑scale simulation and experiment data
AI evaluation and benchmarking (model quality, robustness, scientific validity)
Strong domain literacy in molecular physics / computational chemistry and how it maps into AI/ML workflows
Comfort operating as a bridge between scientific computing, software engineering, and product management
Minimum Qualifications
PhD in Computational Chemistry, Materials Science, Physics, or a closely related field with a strong focus on molecular modeling or quantum simulations.
Hands‑on experience with automated quantum simulation workflows (e.g., DFT pipelines for materials or molecules).
Demonstrated understanding of real‑time AI inference systems, ML model deployment, or AI platform operations.
Experience working closely with engineering teams on CI/CD, data pipelines, and scalable computing systems.
Ability to translate complex research outputs into clear product requirements and platform features.
Strong communication skills with experience collaborating across AI, engineering, and scientific R&D teams.
Preferred Qualifications
Prior experience in product management or a product‑like role in AI infrastructure, ML platforms, or scientific computing.
Experience guiding architecture and implementation of neural networks and generative models on molecular or battery datasets.
Familiarity with automated DFT for battery materials and integration of quantum chemistry results into ML workflows.
Experience with prompt engineering, fine‑tuning, and evaluation of LLMs, especially for scientific or technical domains.
Knowledge of modern ML and data ecosystems (e.g., Python, PyTorch, JAX, TensorFlow, workflow orchestrators, model registries, feature stores).
Background in or exposure to battery R&D and materials informatics.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Product Management
Services for Renewable Energy
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SES AI Corp. (NYSE: SES) is dedicated to accelerating the world’s energy transition through groundbreaking material discovery and advanced battery management. We are pioneering the integration of cutting‑edge machine learning into battery R&D, and our AI‑enhanced, high‑energy‑density and high‑power‑density Li‑Metal and Li‑ion batteries 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 for transportation (land and air), energy storage, robotics, and drones—truly taking us Beyond Li‑ion™.
To learn more, visit: www.ses.ai
What We Offer
Highly competitive salary and robust benefits, including comprehensive health coverage and attractive equity/stock options in a NYSE‑listed company.
The opportunity to contribute directly to a meaningful scientific mission: accelerating the global energy transition with clear and broad public impact.
A dynamic, collaborative environment at the intersection of AI, computational science, and advanced battery technology.
Significant opportunities for growth as you collaborate with leading experts in AI, R&D, and engineering.
Role Overview We are seeking a Product Manager for AI/ML Computing Infrastructure & Simulation to own the roadmap for SES AI’s scientific computing platform, ML toolchain, and simulation workflows.
This role sits at the intersection of machine learning, computational chemistry, and platform engineering, guiding how automated quantum simulations, real‑time AI inference, and large‑scale molecular workflows are productized and delivered as reliable, usable systems for scientists and engineers.
You will partner closely with AI researchers, computational chemists, software engineers, and business stakeholders to turn cutting‑edge research—such as automated DFT pipelines and generative models for molecular and battery materials—into scalable, production‑grade products aligned with SES’s strategic roadmap.
Key Responsibilities
Own the product vision and roadmap for the AI/ML computing and simulation platform, spanning quantum simulations, molecular modeling, and AI‑assisted workflows.
Translate scientific and R&D requirements into clear platform capabilities, milestones, and prioritized backlogs.
Align research outputs with platform usability, reliability, and business impact, ensuring delivery against roadmap commitments.
Infrastructure, Toolchain & Workflows
Define requirements for automated quantum simulations (e.g., automated DFT workflows for battery materials) and their integration into the core platform.
Shape the architecture and user experience of real‑time AI inference systems serving molecular and battery models.
Collaborate with engineering leads on CI/CD pipelines, data streaming, and orchestration for simulation and AI workflows.
Guide integration of tools across the ML stack (training, evaluation, deployment, monitoring) into cohesive, data‑driven workflows.
Work with ML researchers to design and productize neural networks and generative AI models trained on molecular and battery datasets.
Define requirements for AI evaluation, benchmarking, and monitoring, including performance, robustness, and scientific relevance.
Incorporate domain-level understanding of molecular physics and computational chemistry into AI workflows to ensure scientific validity.
User Experience & Stakeholder Engagement
Serve as the connective tissue between computational chemists, platform engineers, AI researchers, and product leadership.
Gather and synthesize user feedback from scientists and engineers to continuously refine platform features, APIs, and UX.
Communicate roadmap, trade‑offs, and release plans to technical and non‑technical stakeholders.
Governance & Performance
Define and track KPIs for platform usage, computational scalability, AI engine performance, and time‑to‑insight for R&D teams.
Drive best practices for reliability, observability, and performance optimization of AI and simulation engines.
Core Competencies
Automated quantum simulations (e.g., high‑throughput, scripted DFT or ab initio workflows)
Real‑time AI inference and serving of ML models in production environments
Computational scalability and performance optimization of AI engines and simulation workloads
CI/CD for ML and scientific computing, including automated testing and deployment
Data streaming and data pipeline design for large‑scale simulation and experiment data
AI evaluation and benchmarking (model quality, robustness, scientific validity)
Strong domain literacy in molecular physics / computational chemistry and how it maps into AI/ML workflows
Comfort operating as a bridge between scientific computing, software engineering, and product management
Minimum Qualifications
PhD in Computational Chemistry, Materials Science, Physics, or a closely related field with a strong focus on molecular modeling or quantum simulations.
Hands‑on experience with automated quantum simulation workflows (e.g., DFT pipelines for materials or molecules).
Demonstrated understanding of real‑time AI inference systems, ML model deployment, or AI platform operations.
Experience working closely with engineering teams on CI/CD, data pipelines, and scalable computing systems.
Ability to translate complex research outputs into clear product requirements and platform features.
Strong communication skills with experience collaborating across AI, engineering, and scientific R&D teams.
Preferred Qualifications
Prior experience in product management or a product‑like role in AI infrastructure, ML platforms, or scientific computing.
Experience guiding architecture and implementation of neural networks and generative models on molecular or battery datasets.
Familiarity with automated DFT for battery materials and integration of quantum chemistry results into ML workflows.
Experience with prompt engineering, fine‑tuning, and evaluation of LLMs, especially for scientific or technical domains.
Knowledge of modern ML and data ecosystems (e.g., Python, PyTorch, JAX, TensorFlow, workflow orchestrators, model registries, feature stores).
Background in or exposure to battery R&D and materials informatics.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Product Management
Services for Renewable Energy
Referrals increase your chances of interviewing at SES AI by 2x
Get notified about new Product Manager jobs in
Greater Boston .
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
#J-18808-Ljbffr