Founding Machine Learning Researcher / Engineer

Founding Machine Learning Researcher / Engineer

I'm supportin a stealth AI4Science start-up that is building foundation models that unify molecular design, physical reasoning, and automated experimentation. They are developing large-scale systems that can understand, generate, and evaluate molecules and materials by combining data-driven learning with the underlying rules of chemistry and physics.

You will be part of a small, high-talent founding team backed by leading investors in AI and life sciences. The group includes researchers and engineers who have made pioneering contributions across biomolecular modeling, chemical synthesis, electronic structure, and large-scale molecular ML systems. They are supported by leading venture investors in AI and life sciences with a proven track record in scaling AI-for-science teams. This is an opportunity to help define the core capabilities, culture, and technical direction from day one.

The Role
In this role, you'll help define how modern AI engages with molecular and materials discovery-from first principles through deployment. Depending on your background, your work may include:

• Building and scaling foundation models that learn rich molecular and biomolecular representations, support generative design, and enable scientific reasoning across large chemical and structural spaces.
• Blending physics with learning, developing model components that incorporate molecular interactions, electronic structure, and reaction behavior to improve prediction and guide generative output.
• Creating the bridge to a self-driving lab, developing interfaces that allow models to propose, prioritize, and execute experiments. This includes uncertainty estimation, experiment selection, and closed-loop optimization with automated physical systems.
• Establishing rigorous evaluation, defining metrics for chemical soundness, synthesizability, and downstream experimental usefulness to ensure generated candidates translate into real-world impact.
• Standing up the ML backbone, including training infrastructure, serving environments, and data pipelines that support fast iteration, large-scale experiments, and integrated simulation-to-lab workflows.
• Working AI‑first, using state-of-the-art AI-assisted coding, analysis, and research tools to accelerate exploration, increase modeling velocity, and deepen scientific insight.

This role offers substantial ownership and influence over the technical agenda and the long-term direction of the platform.

Qualifications

• PhD or equivalent experience in machine learning, computational chemistry, physics, or a related scientific field.
• Experience training or improving large ML systems, including generative models, representation learning architectures, or foundation models.
• Comfort working with structured, noisy, or experimental scientific datasets, and translating scientific questions into ML formulations.
• Strong engineering fundamentals and comfort with complex, evolving codebases.
• A mindset oriented toward rapid iteration, clear reasoning, and collaborative problem-solving.
• Familiarity with modern AI-assisted tools and a desire to integrate them throughout the research workflow.

Why Join

• Ground-floor ownership: Help shape the core architecture, scientific direction, and culture.
• Top-tier backing: Supported by leading venture investors in AI and life sciences with a proven track record in scaling AI-for-science companies.
• Work with pioneers: Collaborate with researchers who helped define modern molecular modeling, synthesis planning, and physics-informed ML.
• Real-world impact: Your models will directly guide physical experiments through automated lab systems.
• High-talent environment: A small, deeply technical team with an ambitious mission.
• Meaningful equity: Early-stage ownership aligned with long-term success.