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PhD Research Intern

Palo Alto, CA · On-site

What you'll do As a PhD intern, you will ... Collaborate with research scientists to advance methods in: * Planning and RL for computer use (e.g ...

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How much do materials science phd research jobs pay per year?

As of Sep 13, 2026, the average yearly pay for materials science phd research in the United States is $95,315.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,000.00 and $109,000.00 per year, depending on experience, location, and employer.

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For Materials Science Phd Research jobs, the most frequently searched job titles are:

Principal Scientist / Associate Director, Agentic AI Research for Materials Science

Cambridge, MA • On-site

Lila Sciences
See more jobs at Lila Sciencesexternal

Full-time

Re-posted 4 days ago

Job description

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.

Apply