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Periodic Labs

AI-driven materials discovery and design

Research Intern - Thin Films

Physical SciencesConfirmed live in the last 24 hours
Internship · Winter 2026Menlo Park, CA, USANo salary listed

About the Job

Requirements

  • Currently pursuing or recently completed a Bachelor's, Master's, or PhD in electrical engineering, materials science, applied physics, mechanical engineering, chemical engineering, chemistry, or a related field.
  • Hands-on experience in a university cleanroom, nanofabrication facility, semiconductor lab, photonics lab, MEMS lab, or related research environment.
  • Practical exposure to lithography, deposition, and etch processes, with an understanding of how these steps connect in a device fabrication flow.
  • Experience working with silicon-based microelectronic, photonic, MEMS, optical, or semiconductor devices.
  • Ability to work at fabrication tools, follow cleanroom protocols, log process details, and learn new equipment.
  • Ability to work independently in shared research facilities while escalating issues appropriately and learning from senior team members.

Responsibilities

  • Execute hands-on nanofabrication processes for silicon-based microelectronic, photonic, MEMS, or optical devices, including lithography, thin-film deposition, etch, substrate preparation, and related cleanroom workflows.
  • Support lithography processes including spin coating, baking, mask alignment, exposure, development, lift-off, and optical inspection.
  • Operate or support thin-film deposition processes including sputtering, evaporation, physical vapor deposition, pulsed laser deposition, atomic layer deposition, plasma-enhanced chemical vapor deposition, or related techniques.
  • Run or assist with etch processes including wet etching, plasma etching, reactive ion etching, inductively coupled plasma reactive ion etching, or other dry etch workflows.
  • Prepare substrates, manage process flows, track samples, and maintain experimental records meeting metadata and data quality standards for AI training.
  • Perform structural, optical, electrical, or morphological characterization, potentially including profilometry, ellipsometry, scanning electron microscopy, optical microscopy, X-ray diffraction, X-ray reflectivity, four-point probe, or related metrology.
  • Work with senior scientists and engineers to troubleshoot process issues, identify failure modes, and iterate on recipes or workflows.
  • Collaborate with materials science, AI, and automation teams to connect fabrication results with model predictions and experimental planning.
  • Follow laboratory safety and facility protocols, including requirements for external shared cleanroom facilities.

Desired Qualifications

  • Experience with full or partial device fabrication flows involving lithography, deposition, etch, and characterization.
  • Hands-on experience with mask aligners, spin coaters, sputtering, evaporation, atomic layer deposition, plasma-enhanced chemical vapor deposition, reactive ion etching, inductively coupled plasma reactive ion etching, profilometry, ellipsometry, scanning electron microscopy, or four-point probe.
  • Experience fabricating silicon photonics, MEMS, optoelectronic devices, sensors, thin-film devices, or other micro- or nanoscale structures.
  • Experience in shared cleanroom environments, including facility-specific safety training, tool reservation systems, and shared-use norms.
  • Exposure to functional materials in thin-film or device form, including oxides, nitrides, metals, semiconductors, superconductors, magnetic materials, ferroelectrics, or multilayer stacks.
  • Interest in experimental science, nanofabrication, and AI-driven discovery.

About the Company

Periodic Labs

AI-driven materials discovery and design

Periodic Labs uses artificial intelligence to model, predict, analyze, and design new materials. Its platform learns from large scientific datasets to suggest viable material compositions, structures, and processing methods that meet performance targets. The team combines AI expertise from OpenAI and DeepMind, applying it specifically to materials science to offer data-driven material design for energy, electronics, and manufacturing. The company aims to accelerate materials discovery, enabling clean energy, better semiconductors, and stronger supply chains by reducing the time and cost of traditional research.

Company size
51-200
Company stage
Seed
Total funding
$300M
Headquarters
Menlo Park, CA
Founded
2025

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