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Salary: USD 228,000 - 358,000 / annual
Lila Sciences is building Scientific Superintelligence to solve major challenges in biomedicine by combining advanced AI models with proprietary instruments that execute the scientific method autonomously. Within Life Sciences AI, you will work at the intersection of mechanistic modeling and experimental design to build reasoning systems that decompose biological hypotheses, judge existing evidence, and identify what remains open.
As Scientist II / Senior Scientist, BioML, you will own three interconnected areas:
**Mechanistic Reasoning & Evaluation:** Encode how good scientists decompose hypotheses into structured frameworks and reasoning workflows. Define what constitutes a good answer and build evaluations that measure whether systems reach conclusions for the right reasons, not just plausible-sounding ones.
**Biological Data Analysis:** Conduct hands-on analysis of single-cell omics, perturbation screens, proteomics imaging, and genetic data. Build reproducible pipelines that transform raw measurements into model-ready evidence, perform quality control and annotation-stability analyses, and produce quantitative results that ground reasoning systems.
**Experimental Campaign Design & Loop Closure:** Specify what to measure, in which contexts, at what precision and scale. Design experiments that anchor systems in real data and generate outcome-verified cases for training and evaluation. Analyze results, update assessments, and communicate what changed.
Additional responsibilities include building outcome-verifiable benchmarks with enforced evidence boundaries, co-designing evaluations with ML scientists and experimental biologists, and communicating results completely to scientific, engineering, and therapeutic audiences—including findings that do not support desired conclusions.
This is an individual contributor role emphasizing execution, scientific judgment, and cross-functional collaboration rather than group leadership. You will work closely with experimental scientists, ML researchers, and platform engineers in a fast-moving, interdisciplinary environment.
**Requirements:**
- PhD in Computational Biology, Bioinformatics, Computational Genomics, Biostatistics, Machine Learning, or related quantitative field, with research centered on biological data
- Strong hands-on computational and data-analysis depth: fluent in Python with substantial experience analyzing high-dimensional biological data (single-cell omics, perturbation screens, imaging-based proteomics, or genetics) in reproducible, version-controlled pipelines
- Mechanistic biology fluency: ability to reason about pathways or drug mechanisms step-by-step, understanding what is rate-limiting, what would be observed, what evidence distinguishes alternatives, and what assays can and cannot establish
- Evidence judgment with instinct to systematize it: demonstrated ability to assess whether data support claims and reason about what was knowable when, combined with interest in making that judgment reproducible through structure, schemas, or evaluation
- Clear communication: ability to explain methods, assumptions, results, and limitations to both technical and interdisciplinary audiences
**Bonus qualifications:**
- Deep analysis experience in perturbation screens, single-cell omics, imaging-based proteomics, or functional genomics
- Depth in immune cell biology, cell therapy, or targeted delivery (receptor engagement, trafficking, effector function)
- Experience with lab-in-the-loop or closed-loop workflows, active learning, or experiment selection
- Experience evaluating language models or agents on scientific judgment tasks, including contamination and memorization controls
- Experience designing or applying structured evidence frameworks, curated resources, or controlled vocabularies
