ML Scientist - Scientific Reasoning
Skills
- Advanced Python programming and software development for ML systems.
- Deep experience with LLM frameworks: PyTorch, HuggingFace Transformers, LangChain, LlamaIndex, etc.
- Expertise in LLM reasoning methods: in-context learning, chain-of-thought, test-time compute, tool-augmented reasoning.
- Ability to design experiments, prototype models, and iterate rapidly on research ideas.
- Strong collaboration and communication with interdisciplinary teams (scientists, engineers).
Responsibilities
- Design and formalize LLM-based scientific reasoning frameworks (structured prompting, reasoning chains, test-time compute).
- Research and implement in-context learning, self-reflection, and adaptive reasoning methods for discovery workflows.
- Prototype and build scalable model systems that integrate with Lilaās platforms for real scientific problems.
- Collaborate with domain scientists and engineers to encode domain knowledge and combine symbolic and statistical approaches.
- Balance theoretical research with practical ML engineering to move ideas into production-capable systems.
Education
- PhD in Computer Science, Machine Learning, AI, Engineering, Materials Science or related preferred.
- Equivalent industry research experience considered in lieu of a PhD.
Benefits
- Competitive salary and potential equity opportunity
- Collaborative research-focused environment with interdisciplinary teams
- Flexible time off and work-life balance (hybrid onsite/remote model)
- Opportunities to publish and contribute to open-source frameworks
- Professional development and conference support
Average salary estimate
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