2 months ago
Berlin, Germany +3 moreMid Level
Responsibilities
- Design the agentic framework for literature-grounded hypothesis generation, simulation workflows, computational validation, and experimental validation.
- Integrate agents with ML models, simulation engines, databases, and heterogeneous compute backends.
- Build pipelines for agents to plan, schedule, execute, and interpret computational tasks at scale and over long periods.
- Use expert chemistry annotations to improve agent planning, retrieval, and decision-making.
- Create evaluations to measure agent effectiveness.
- Build experimental-design agents using Bayesian optimization, active learning, and related sequential decision-making methods.
- Develop multi-fidelity and multi-objective strategies that balance cost, time, and uncertainty across simulations and physical experiments.
- Close the loop between simulation and physical experiments so outcomes become durable knowledge for future agent reasoning.
- Collaborate with chemists, materials scientists, and the Agent team on core orchestration intelligence.
- Work on customer projects and implement their direct technical needs.
Requirements
- Proficiency in a modern ML ecosystem such as PyTorch or JAX and experience taking ML-driven systems from prototype to production.
- Strong software engineering skills for building scalable production systems, including testing, modular design, and scalable ML operations.
- A PhD or Master’s degree with ideally 4–5 years of industry experience; candidates with a PhD and slightly less industry experience may also be considered.
- Interest in applying AI to scientific and materials-discovery challenges.
- Willingness to learn materials science and experimental chemistry terminology.
- Experience with LLM-assisted programming and a thorough understanding of its strengths and weaknesses.
- Preferred experience applying ML to materials science, chemistry, or drug discovery.
- Preferred experience with agentic frameworks and LLM-powered applications.
- Preferred experience with Bayesian optimization, active learning, bandits, or reinforcement learning in real-world systems.
- Preferred experience with planning models, self-improving systems, multi-tool agents, or RLHF/RLAIF workflows.
Benefits
- Competitive salary and equity in CuspAI.
- 28 days of holiday in Germany, the Netherlands, and the UK, or 21 days in Japan, Singapore, and the US, in addition to local public holidays.
- Paid parental leave of 26 weeks for primary caregivers and 12 weeks for secondary caregivers.
- Professional development budget.
- Office-based work three days per week in Cambridge, London, Amsterdam, or Berlin, with regular travel to other offices potentially required.
- Interdisciplinary collaboration with AI researchers, computational chemists, materials scientists, and experimental scientists.
Tech Stack
Categories
About CuspAI
CuspAI builds an AI-driven materials discovery platform that helps R&D teams in chemicals, energy, and manufacturing design and evaluate novel molecules and materials. The company offers software and models accessed via cloud APIs and collaborations to prioritize candidates, guide experiments, and shorten lab cycles. Founded in 2024 and headquartered in Cambridge, it is a privately held startup focused on applying generative and physics-informed AI to sustainability and next‑generation materials problems.