Machine Learning Scientist I/II, Scientific Reasoning

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

Offered From the posting source checked Jun 20, 2026

Verification

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Salary

$176K-$304K From the posting source checked Jun 20, 2026

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

114% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source checked Jun 20, 2026

Application

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Scientist I/II, Scientific Reasoning Cambridge, MA USA Your Impact at LILA As a Machine Learning Scientist focused on Scientific Reasoning, you will help pioneer the next generation of AI systems capable of reasoning like a scientist. You'll design novel frameworks that push the boundaries of LLM-based reasoning methods - while also implementing scalable frameworks that integrate with Lila's platforms. This role bridges deep theoretical thinking with practical ML engineering, enabling breakthroughs in how scientific hypotheses are generated, tested, deployed and optimized. What You'll Be Building - Design and formalize frameworks for scientific reasoning with LLMs , including structured prompting, reasoning chains, and test-time compute. - Explore and implement methods for in-context learning, self-reflection, and adaptive reasoning in scientific discovery workflows. - Build scalable model prototypes that can be deployed to solve frontier scientific problems. - Collaborate with scientists and engineers to encode domain knowledge into reasoning systems that integrate symbolic and statistical approaches. What You'll Need to Succeed - PhD (preferred) or equivalent research/industry experience in Computer Science, Machine Learning, AI, Engineering, Materials Science or related fields. - Strong programming skills in Python with deep expertise in LLM frameworks (PyTorch, HuggingFace Transformers, LangChain, LlamaIndex , and related toolkits). - Expertise in LLM reasoning methods : in-context learning, test-time compute, chain-of-thought, or tool-augmented reasoning. - Ability to balance theoretical research with practical ML engineering to deliver scalable solutions. Bonus Points For - Research experience in causal reasoning, symbolic AI, or probabilistic programming . - Contributions to open-source LLM reasoning

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