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 |
|---|---|---|
| Staff / Principal Research Engineer, AI Safety, Technical Mitigations This role focuses on building and implementing AI safety strategies and systems for the safe deployment of scientific capabilities, involving technical safety strategy development, safety-focused evaluations, and a safety research agenda. It requires experience in building safety systems, classifiers, or post-training for frontier-class problems and scalable production systems. | Post-trainEval Gate | 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 |
| Research Engineer, Frontier Capabilities Research Engineer focused on training LLMs for long-horizon scientific discovery tasks, spanning the post-training stack from SFT to asynchronous RL on agentic harnesses. The role involves designing, building, and optimizing systems for scaling post-training, sharpening reasoning, and enabling compute-intensive agentic-harness training. Specific work streams include GPU optimization, stack and infrastructure development, model experimentation, evaluations and benchmarks, and agentic capabilities research. | Post-trainAgent | 9 |
| Research Product Manager, Post Training Research Product Manager to set the vision for Lila’s foundational models, defining capabilities and performance that turn breakthrough research into real-world scientific impact. Owns the capability roadmap for core foundational model releases, synthesizing input from various stakeholders into a prioritized roadmap. Defines evaluation criteria, success metrics, and gating criteria for promoting models to production. Partners with research and model-training leads, and maintains documentation. | Post-trainEval Gate | 8 |
| Senior / Staff Machine Learning Engineer, Applied AI Senior/Staff Machine Learning Engineer at Lila Sciences focused on improving AI models for customer-specific scientific needs by training, evaluating, and deploying LLMs and multi-modal models. The role bridges research and engineering, translating frontier capabilities into reliable, production-quality systems and workflows. | Post-trainAgent | 8 |
| 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, Machine Learning for Digital Twins The role involves building, training, and evaluating ML models for physical and experimental systems, focusing on digital twins, operator learning, surrogate modeling, and uncertainty quantification. The work directly influences the design and operation of AI Science Facilities. | Post-train | 7 |
| Co-Op, Data Extraction The role involves contributing to AI systems for knowledge extraction from scientific literature and patents. Responsibilities include fine-tuning and evaluating language/multimodal models, building data structuring pipelines, running extraction pipelines, analyzing results, and documenting findings. The goal is to ship work that integrates into production systems. | Post-trainServe | 7 |