Director / Senior Director, AI x Lab Integration

Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to accelerate scientific discovery. Within AI Research, the AI x Lab Integration team develops the capabilities that allow reasoning models to translate scientific intent into executable experiments.

We are seeking a Director, AI x Lab Integration to further grow and lead this team. You will set the research agenda for models that understand the purpose of a protocol, compose experiments against real laboratory constraints, diagnose failures, and adapt protocols as evidence arrives. Your work will help LLMs hone scientific judgment and learn from practical knowledge needed to turn a research question into a reliable experimental workflow.

This is a hands-on research leadership role. You will recruit exceptional researchers, make consequential technical decisions, and establish rigorous evidence that model improvements transfer to real experiments. You will partner closely with domain scientists and the teams responsible for laboratory execution, data infrastructure, and orchestration.

What You'll Be Building

- The lab intelligence research agenda: Lead research across protocol understanding, intent-to-protocol generation, feasibility assessment, protocol repair, failure diagnosis, and adaptation based on experimental results.

- Learning from scientific expertise: Partner with the scientists to turn expert demonstrations, corrections, and experimental feedback into model improvements that capture tacit scientific knowledge.

- Post-training methods for laboratory reasoning: Lead research in supervised fine-tuning, reinforcement learning, tool use, and learning from execution feedback. Determine which methods improve scientific validity, execution success, and generalization to unfamiliar protocols and conditions.

- Rigorous capability evaluation: Establish independent evaluations spanning individual protocol skills, decisions made during a run, and workflows that cross scientific domains. Measure scientific correctness, feasibility, execution outcomes, uncertainty, and the need for human intervention.

- A high-performing research team: Hire, mentor, and lead scientists and research engineers. Set clear ownership, maintain strong experimental standards, and build a culture of ambitious hypotheses, careful measurement, and rapid learning.

What You'll Need to Succeed

- PhD in Machine Learning, Computer Science, Robotics, Computational Science, or a related field, or equivalent research and leadership experience.

- A substantial track record of leading original AI research and translating it into capabilities that work beyond a benchmark.

- Demonstrated experience developing AI capabilities for automated laboratories, scientific agents, or closed-loop experimentation, with model decisions informed by real experimental results.

- Experience building and leading research teams, including recruiting strong scientists and engineers, developing technical leaders, and setting priorities under uncertainty.

- The ability to engage deeply with experimental scientists and translate scientific objectives, controls, and failure modes into tractable research problems.

- A hands-on leadership style: comfortable reviewing model behavior, challenging research designs, interpreting failure cases, and guiding technical decisions.

Bonus Points For

- Deep technical expertise in foundation models, post-training, reinforcement learning, agent learning, or closely related methods.

- Research in procedural reasoning, program synthesis, planning, causal reasoning, or uncertainty-aware decision-making.

- Experience in methods for learning from expert demonstrations, sparse feedback, or costly real-world experiments.

- Deep domain expertise in experimental biology, chemistry, materials science, or laboratory automation.

- A record of influential publications, open-source contributions, or deployed research systems in AI or AI for Science.

- Experience grow