The OpportunityWorld-class physics models are only as powerful as the workflows that steer them. As the lead on Mirror's reasoning team, your role is to design and engineer intelligent systems that understand how to run scientific simulation frameworks such as quantum chemistry and molecular dynamics, interpret the results, and adapt future actions. Your work will unlock autonomous R&D loops that plan experiments, allocate compute, and derive insights in real time, enabling state-of-the-art simulation methods to be widely and efficiently applied to industrial-scale problems.
Key Responsibilities- Architect LLM-based intelligent agents that plan, schedule, and monitor multistep simulation campaigns (DFT, ab-initio MD, reactive force-field, continuum).
- Design reinforcement-learning or curriculum-learning loops that teach agents to balance exploration, accuracy, and cost across heterogeneous compute resources.
- Integrate domain-specific knowledge graphs, symbolic reasoning engines, and uncertainty estimators.
- Develop analytic tooling that explains agent decisions, determines root causes for simulation failures, and quantifies downstream business impact.
- Build robust orchestration pipelines (Ray/Kubernetes/SLURM) for on-prem HPC and cloud GPU clusters; implement fault tolerance and provenance tracking.
- Collaborate with foundation-model and multimodal teams to couple high-level reasoning with foundational atomistic predictions.
- Engage with the AI-for-science community through publications and contributions at NeurIPS, ICML, ICLR, or other domain venues.
Who you are- Ph.D. or M.S./B.S. with equivalent research record in Computer Science, Applied Math, or related field with emphasis on reasoning and LLMs.
- 3+ years research experience building decision-making or agentic frameworks at scale.
- Fluency in Python plus modern ML stacks (PyTorch/JAX) and familiarity with distributed training tooling (CUDA, NCCL, Slurm/K8s/Ray).
- Publications in top-tier ML or domain conferences/journals.
- Strong publication or open-source track record in ML for physical sciences.
- Excellent collaboration, communication, and team-working skills.
- Deep commitment and passion for advancing science.
Preferred Extras- Familiarity with quantum chemistry, atomistic simulation, or chemistry/materials science
- Familiarity with active learning, retrieval-augmented generation, or agentic workflows for scientific automation.
- Prior experience aligning language models with scientific knowledge bases or ontologies.
What We Offer- Competitive salary + meaningful equity
- Full health, dental, and vision benefits for you and your family
- Personal fitness budget
- Unlimited PTO and all national holidays
Location & Work ModelHybrid work available; in-office preferred. Visa sponsorship available.
