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AI Product Leader

AI Product Leader

DeepRec.aiBoston, MA, US
2 days ago
Job type
  • Full-time
Job description

Senior AI Product Lead <>

$200M Backed Materials Discovery Platform <>

USA

I'm representing a DeepTech organisation working at the intersection of advanced AI, computational chemistry, and large-scale simulation.

They are seeking an Senior Leader who can guide the platform behind their proprietary AI-driven discovery engine for molecular and electrolyte design. This system supports automated quantum simulations, real-time inference, and high-throughput evaluation for next-generation battery materials.

Your role

  • Define and execute the roadmap for simulation infrastructure, model training, serving, and CI CD across both AI and scientific computing
  • Architect systems that combine physics-based models, computational chemistry, multi-physics simulation, and machine learning
  • Support design and optimisation of workflows that connect molecular prediction, materials screening, and electrolyte design
  • Build real time inference pipelines and data frameworks that evaluate AI generated molecular or material candidates
  • Implement scalable data streaming, orchestration, and automated simulation systems for heavy computational workloads
  • Optimise GPU and CPU performance for quantum simulations, molecular modelling, and ML pipelines
  • Collaborate with scientists across physics, chemistry, materials science, and battery R&D to translate research into product features
  • Support experiment design and validation cycles by integrating lab based testing feedback into platform capabilities
  • Lead and mentor a small team across engineering, modelling, and ML

What we're looking for

  • PhD level experience in computational science, quantum chemistry, molecular simulations, materials science, chemical engineering, applied physics, or a related field
  • Strong background in computational modelling such as DFT, molecular modelling, and multi physics simulation
  • Hands on experience in ML infrastructure, including training, serving, inference, and data pipelines
  • Knowledge of AI or ML applied to materials design, property prediction, or structure property modelling
  • Experience with high performance computing on GPU clusters or distributed systems
  • Domain literacy in battery chemistry, electrolyte materials, or broader materials science
  • Familiarity with experimental validation cycles, electrochemical testing, or lab based material evaluation is helpful
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    Product • Boston, MA, US

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