Your Impact at LILA
Own the technical direction for agentic AI systems applied to materials science at Lila. You will set and execute the roadmap for autonomous agents that plan, run, and interpret materials experiments, based on understanding of internal knowledge and state-of-the-art research work in public literature. Your work shifts materials research from human-paced iteration to machine-paced experimentation through scientific reasoning and understanding.
This is a player-coach role on the PS AI team. You will lead a small group of scientists and engineers, set the bar for scientific rigor and engineering quality, and partner with diverse teams so that agentic systems land on real programs. You will own the trade-offs between research ambition and production reliability, and represent the agentic-AI direction to technical leadership.
The work spans foundational research and applied delivery. You will publish where the science merits it, ship systems that materials teams depend on, and shape how Lila scales agentic capabilities across its materials portfolio.
What You'll Be Building
- Roadmap and direction. Define and execute the agentic AI roadmap for materials science, including agentic frameworks and retrieval-augmented generation for understanding multi-modal research data from research literature and other data sources.
- Agent system architecture. Lead the design of agentic frameworks grounded in fundamental scientific understanding and the state of the art, and deliver end-to-end systems on real-world projects.
- Team leadership. Hire, mentor, and grow a small cross-functional team of scientists and engineers; set the bar for scientific rigor, code quality, and reproducibility.
- Cross-team partnership. Partner with diverse teams at Lila to push the state of the art and deliver systems that integrate with experimental infrastructure and land on real programs.
- Research currency and external voice. Track state-of-the-art in agentic AI, scientific ML, data extraction, and reasoning models; translate external advances into internal direction, and publish or present where the science merits it.
What You'll Need to Succeed
- PhD in Computer Science, Machine Learning, Materials Science, Chemistry, Physics, or a related field, with 5+ years of post-PhD research and applied ML experience.
- Track record of building and shipping agentic systems, ML pipelines, or autonomous research workflows that delivered measurable scientific or product impact.
- Deep expertise across modern ML, NLP, and reasoning: LLMs, agentic frameworks, tool use, planning, data extraction, and multi-modal data.
- Working knowledge of materials science, computational chemistry, or condensed-matter physics sufficient to ground agent behavior in real scientific constraints.
- Proficiency in Python and the ML software stack, with strong engineering habits around reproducibility, testing, and production deployment.
- Experience leading scientists and engineers: setting technical direction, hiring, mentoring, and developing team members.
- Clear written and verbal communication; able to translate between research, engineering, and program stakeholders.
Bonus Points For
- Publications, patents, or open-source contributions in agentic AI, scientific ML, or autonomous research systems.
- Experience integrating agents with real-world materials science tasks and familiarity with materials data representations and ontologies.
- Production experience with workflow orchestration and distributed compute on cloud or HPC.
- Community recognition: invited talks, conference organizing, or community leadership in agentic AI or scientific AI.
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
$288,000—$420,000 USD
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We’re All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
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A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.