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 growing a small research team into a program with sustained scientific and operational impact.

Compensation

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.

U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.

International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.

Expected Base Salary Range $360,000 - $570,000 USD

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