AI Product Leader
Summary
Lead AI product innovation at DeepRec.ai, a DeepTech company. Postgraduate degree required. Master ML infrastructure, GPU clusters, molecular modeling, and quantum chemistry to drive next-generation computational solutions.
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
