Lila Sciences
AI Frontier · AI scientific discovery
AI Frontier · AI scientific discovery
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 |
|---|---|---|
| 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 |
| Principal, Machine Learning Engineer Principal ML Engineer at Lila Sciences, focusing on building and scaling ML infrastructure for generative models in medicine. The role involves owning end-to-end systems from training pipelines and distributed compute to model deployment and integration into a closed-loop discovery engine. Key responsibilities include designing and optimizing large-scale training pipelines, owning production ML systems, architecting ML infrastructure, driving the "Lab-in-the-Loop" lifecycle, and defining ML engineering standards. The role requires deep expertise in distributed training, production ML systems, and strong software engineering fundamentals, with a focus on generative models for biological data. | DataServe | 9 |
| Director, Data Platform Engineering Director of Data Platform Engineering to lead a team responsible for Lila's product data platform, owning the end-to-end architecture, delivery, reliability, and developer/data scientist experience. The platform supports analytical and ML workloads, including AI inference workflows. The role involves team leadership, technical strategy, stakeholder management, and driving innovative solutions for data interfaces, exploration, query, analytics, and ML/inference at scale. | DataServe | 7 |
| Director of Product, Life Sciences Director of Product, Life Sciences at Lila Sciences, responsible for shaping the future of therapeutic R&D by leading the strategy, roadmap, and execution for products and platforms at the intersection of drug discovery, chemical synthesis, and scientific intelligence. This role drives AI capabilities towards breakthrough solutions, translates strategic objectives into development campaigns, and leads cross-functional teams. | Data | 7 |
| Senior Software Engineer, ML Research Senior Software Engineer to build and maintain ML libraries, tools, and research infrastructure, focusing on performance, security, and MLOps. The role involves designing libraries, CI/CD pipelines, and supporting compute environments, with a strong emphasis on software engineering best practices within an ML research context. | Data | 7 |