AI Product Leader

Qualifications

• 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

Responsibilities

• 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

Full 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