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KronosAI Tech Stack
Foundation models for physics simulations and scientific computing
KronosAI builds foundation models trained on physics simulations, targeting the intersection of AI and scientific computing. The stack reveals a deep ML infrastructure play: distributed training (DeepSpeed, Megatron-LM, NCCL, MPI), simulation engines (COMSOL, Ansys, Lumerical, HFSS), and production serving (Ray, Triton). The hiring mix is heavily skewed toward senior research and engineering roles—typical for early-stage physics/ML ventures still defining their core model architecture and product-market fit.
Tech Stack 31 technologies
Core StackLangChain Python React TypeScript Flask Next.js Tailwind CSS AWS Vercel Vue Svelte JavaScript Figma OpenMP NCCL MPI DeepSpeed Megatron-LM Ray C/C++ Triton NVIDIA Nsight VTune LlamaIndex Lumerical COMSOL HFSS Ansys Radix UI CSS+1 more
What KronosAI Is Building
◆Challenges
- Scaling large deep learning models
- Broadening simulation scope
- Expanding research team
- Making simulation tools accessible to non-experts
- Reducing specialized training requirement
▲Active Projects
- Foundational models for physics simulation
- Research directions in numerical pdes and mathematical foundations of scientific machine learning
- Agent-native workflows for autonomous simulations
- Core infrastructure for ai foundation models
- Exascale numerical simulators for physics-based modeling
- Ai-first platform for numerical simulators
- Core product experience
- Ai agent orchestration
- Interactive 3d visualization platform
- Ai-first platforms to interface numerical simulators with ai agents
Hiring Activity
9 roles · 5 in 30d
Department
Seniority
Company intelligence
