Senior / Staff Machine Learning Engineer, Applied AI

Your Impact at LILA We are growing our Applied AI org and seeking talented Senior/Staff Machine Learning Engineers with expertise in LLM training, evaluation, and production-oriented ML systems. You’ll work on improving Lila’s AI models for customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated, and used in real customer contexts. This is a rare chance to join an early team with the autonomy, flexibility, and compute to tackle frontier science problems. Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through experimentation, and ensure model behavior works well end to end inside the application. This role is ideal for someone who can bridge research and engineering: training or adapting models, building evaluation loops, debugging model behavior, and collaborating across AI and Software to move promising capabilities into production-quality systems. What You'll Be Building Close the last-mile gap between Lila AI model capabilities and customer-specific scientific workflows. Build evaluation loops that measure model quality, reliability, and customer fit. Design experiments to improve model performance across applied customer use cases. Feed customer learnings, data signals, and evaluation results back into the Lila AI model improvement cycles. Partner with AI researchers to translate model improvements into usable capabilities. Work with Software to integrate model behavior into end-to-end product workflows. Debug model failures using traces, evaluations, customer context, and scientific feedback. Build reusable tooling for model adaptation, evaluation, and deployment workflows. What You'll Need to Succeed Strong experience building, training, adapting, or evaluating machine learning models. Strong softw