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.
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 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 |
|---|---|---|
| Research Scientist, Dexterous Manipulation & Robot Learning Research Scientist role focused on developing autonomous robotic systems for scientific discovery. This involves pioneering manipulation algorithms using foundation models, RL, diffusion, and human guidance. The role also focuses on human-robot interaction, multi-modal perception, and designing autonomous systems with trust calibration. | AgentPost-train | 10 |
| 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 |
| Sr. Principal / Distinguished ML Scientist, Autonomous Science for Cell Biology Founding Sr. Principal / Distinguished ML Scientist to co-develop scientific direction and own the integration of cell-biology research with Lila's central autonomous-science platform, focusing on foundation models and agentic systems for cellular and tissue biology. | AgentPost-train | 9 |
| Research Scientist I/II, AI for Process Engineering Research Scientist role focused on designing and building AI agent-driven systems for AI-accelerated and AI-orchestrated process engineering in industrial applications. The role involves creating agentic infrastructures for planning, simulating, optimizing, and operating complex physical and chemical processes using existing or ML-driven tools. | Agent | 9 |
| Machine Learning Scientist I/II, Scientific Reasoning Machine Learning Scientist focused on Scientific Reasoning, designing novel frameworks for LLM-based reasoning, exploring in-context learning and self-reflection, building scalable model prototypes, and integrating domain knowledge into reasoning systems. | Agent | 9 |
| 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 |
| Co-op, LLMs for Decision Making Research co-op role focused on developing and evaluating LLM-based decision-making methods for experimental campaigns, combining LLM reasoning with Bayesian optimization and active learning. The role involves prototyping, building evaluation frameworks, and integrating methods into a decision-making stack. | AgentEval Gate | 8 |
| Research Scientist I/II, Statistical Mechanics and Dynamics This role focuses on developing and extending simulation approaches (molecular dynamics, Monte Carlo) for materials discovery, integrating them with AI-driven platforms and agentic AI frameworks. The scientist will build scalable simulation workflows, design methods for coupling simulations with experimental observables, and establish reproducible software pipelines. | Agent | 8 |
| Research Scientist I/II, Multiscale & Multiphysics Simulations Research Scientist role focused on building AI-driven multiphysics and multiscale simulation capabilities for scientific discovery. The role involves developing high-fidelity digital representations of physical systems and integrating them into autonomous discovery and experimental pipelines, with a focus on agent-driven simulation workflows and closed-loop systems. | AgentData | 7 |