Lila Sciences

Join Lila Sciences’ Production Robotics team as a co-op building perception and state-estimation software that helps robots navigate and perform scientific workflows. You’ll work with real sensor data, computer vision, ROS 2, simulation testing, and physical robots.

Own the end-to-end search and retrieval foundation that helps scientists and autonomous agents discover, connect, and cite scientific knowledge. You’ll build semantic search, knowledge graphs, agentic retrieval, and traceable RAG systems used across enterprise scientific workloads.

Build and validate safety mitigations for frontier scientific AI models, agents, tools, and laboratory-connected workflows. This high-priority individual contributor role combines safety research, engineering implementation, evaluation, and integration across model development and serving systems.

Build shared software that helps AI safety researchers evaluate, red-team, and mitigate risks from scientific AI systems. You’ll own backend services, data pipelines, research tools, and model integrations from concept through deployment and ongoing improvement.

Lead security investigations at the intersection of AI safety and security, assessing how advanced AI systems could be misused or compromised. You will develop threat models, test safeguards, and drive evidence-based security improvements for AI and scientific systems.

Build and productionize machine learning models and agentic workflows that accelerate discovery in materials science and chemistry. This role bridges scientific research and reliable, scalable AI/ML platform engineering.

Build shared scientific data and workflow infrastructure that makes ML, simulation, and agent-driven research reproducible, traceable, and reusable. You’ll work closely with scientists and platform teams to turn research prototypes into production-grade Python systems and services.

LILA is seeking a Principal Machine Learning Engineer to lead model post-training, evaluation, and production-oriented ML systems for customer-specific scientific workflows. The role turns frontier model capabilities into reliable, measurable AI behavior through hands-on engineering and collaboration with research and software teams.