Lila Sciences
AI Frontier · AI scientific discovery
AI Frontier · AI scientific discovery
Currently tracking 39 active AI roles, down 40% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $128k–$500k (avg $253k).
Lila Sciences currently has 53 active AI-related job listings. The majority of these roles are in the agents stage, accounting for 43% of the openings. Engineering is the top function for hiring, followed by Research. The company is primarily hiring in the United States. Frequently tagged technologies include agent orchestration, model serving, and evals, suggesting a focus on agent-based AI systems and their deployment. Over the last 30 days, Lila Sciences has posted 12 new AI roles, representing a 25% decrease compared to the previous 30-day period.
Lila Sciences currently has 51 active AI-related roles in our index. The most common open titles are: AI Residency Program, Material Science (2026 Cohort), Co-Op, AI Security, Co-Op, Automation, Co-Op, Autonomous SEM, Co-Op, Data Extraction. Most positions are in Engineering and Research.
Lila Sciences's active AI hiring is concentrated in: agents (43%), data (22%), post-training (20%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Lila Sciences is hiring AI talent in: United States (51 roles).
Job postings at Lila Sciences most frequently mention: Materials Science, Biotech, Machine Learning, Python, Software Engineering.
In the past 30 days, Lila Sciences has posted 10 new AI-related roles. That is a -37% change versus the prior 30 days (16 → 10).
| Title | Stage | AI score |
|---|---|---|
| Senior ML Scientist, Biological Systems Seeking a Senior ML Scientist to build autonomous life science systems that connect AI reasoning, biological evidence, experimental design, and automated execution. The role involves translating scientific direction into working architectures, workflows, and evaluation methods, with a focus on rigorous reasoning about biological hypotheses, proposing experiments, and incorporating evidence to accelerate discovery. This is a hands-on role at the intersection of ML, biological reasoning, agentic systems, and experimental design. | AgentEval Gate | 9 |
| Principal Scientist / Associate Director, Agentic AI Research for Materials Science Principal Scientist / Associate Director, Agentic AI Research for Materials Science. Owns technical direction for agentic AI systems applied to materials science, setting and executing roadmaps for autonomous agents that plan, run, and interpret materials experiments. This player-coach role leads a small team, bridges foundational research and applied delivery, and ships systems for materials teams. Requires PhD, 5+ years post-PhD experience, track record of building/shipping agentic systems, deep expertise in LLMs, agentic frameworks, tool use, planning, data extraction, multi-modal data, and familiarity with materials science. Python and ML software stack proficiency with strong engineering habits are essential. Experience leading scientists/engineers is required. | AgentData | 9 |
| Senior / Principal Scientist, AI for Protein Engineering Senior/Principal Scientist role focused on AI for protein engineering, specifically antibody design and engineering. The role involves developing and executing design workflows, translating biological requirements into ML problems, adapting state-of-the-art AI methods, and collaborating with experimental scientists for validation and active learning loops. The position requires a PhD and strong expertise in both ML and protein biology, with a focus on delivering wet-lab validated biomolecules. | DataPost-train | 9 |
| ML Scientist I / II, Foundation Models for Life Sciences Research Scientist role focused on developing and evaluating foundation models for life sciences applications, including biological sequence design, structure prediction, and multimodal scientific reasoning. The role involves end-to-end ML process contribution, from data generation strategy to feedback loops, and collaboration with experimental scientists. Requires a strong foundation in generative models and ML frameworks, with a PhD or equivalent research experience. | PretrainPost-train | 9 |
| Senior / Principal ML Scientist, Foundation Models for Life Sciences Research Scientist role focused on developing and training large-scale generative foundation models for life sciences applications, including biological sequence design and molecular structure prediction. The role involves end-to-end ML process from problem formulation to integration into a closed-loop discovery engine, with a strong emphasis on research and publications. | PretrainPost-train | 9 |
| Machine Learning Scientist I/II, Multi-Modal Scientific Reasonings Research role focused on advancing multi-modal reasoning with vision-language models (VLMs) on scientific data, including figures, plots, and microscopy. The role involves designing and building state-of-the-art methods for scientific understanding tasks, developing perception modules, and creating datasets and benchmarks. Collaboration with domain scientists and engineers to scale research into production systems is key. | Post-trainAgent | 9 |
| ML Research Scientist I/II, Multimodal Data Extraction Research Scientist role focused on developing foundation models for multimodal data extraction in scientific domains, involving fine-tuning LLMs and vision-language models, and building data pipelines. | Post-trainAgent | 9 |
| Research Scientist, Frontier Capabilities Research Scientist role focused on developing next-generation learning systems and reasoning algorithms for agentic LLMs, particularly in scientific domains with sparse and delayed feedback. The role involves building agentic systems that autonomously propose, execute, and verify scientific hypotheses, or focusing on distillation techniques to create efficient models, or developing scalable experience generation and synthetic data pipelines for training. Requires advanced degree, strong LLM foundation, and ML experiment experience. | Post-trainAgent | 9 |
| Co-Op, LS AI, ML Scientist for Protein Engineering ML Scientist Co-Op role focused on protein engineering research, including generative protein design and antibody engineering. The role involves exploring generative and predictive modeling approaches for biomolecules, analyzing biological datasets, and prototyping workflows that connect model predictions with wet-lab feedback. This is an applied ML research position at the intersection of AI and biology. | Post-train | 8 |
| Co-Op, ML Scientist for Biology Research role focused on developing autonomous-science capabilities in life sciences using AI and automation. The role involves exploring reasoning models, evaluating and reinforcing agentic model behavior, developing benchmark datasets, analyzing multi-modal biological data, and prototyping workflows connecting AI reasoning with scientific feedback. | AgentEval Gate | 8 |
| AI Residency Program, Material Science (2026 Cohort) Research residency program focused on applying AI/ML to materials science, exploring areas like ML-accelerated simulations, generative models, and agentic science. The role involves designing and executing independent research projects, collaborating with scientists, and contributing to open-science initiatives. While publishing is encouraged, the core focus is on advancing scientific discovery. | Pretrain | 8 |
| Scientist I/II, mRNA Translation Dynamics The Scientist I/II, mRNA Translation Dynamics role at Lila Sciences focuses on developing experimental workflows and generating biological datasets that will be used to train machine learning models. The role involves designing and executing high-throughput screening campaigns, optimizing assays, and collaborating with computational and ML teams to define data requirements and validate model predictions. The goal is to integrate synthetic biology, high-throughput experimentation, and intelligent automation to advance biological discovery. | Data | 5 |
| Senior/Principal Scientist, Small Molecule Therapeutics Lead early hit identification efforts in small molecule therapeutics by leveraging expertise in DNA-encoded libraries (DEL) and complementary screening technologies to discover and validate novel chemical matter. Partner cross-functionally with Chemistry, Computational, and Automation teams to develop and scale a small molecule screening platform. Drive hit progression through assay cascades and secure protein sources for characterization. | — | 0 |
| Scientist I/II, Organic Chemistry This role focuses on organic chemistry, developing and optimizing chemical transformations for small molecule synthesis. It involves high-throughput experimentation, reaction workup, and analytical characterization, collaborating with AI/computational teams to build efficient chemistry workflows. | — | 0 |