Research Associate - Thin Films

Research Associate in Thin Films

Join a world-class team of scientists and engineers pushing the boundaries of physical R&D in a groundbreaking lab where AI and automation unlock discoveries at unprecedented speed and scale.

Periodic Labs is developing AI that can both simulate science and verify its predictions to train on the full scientific method. A central challenge in that mission is the gap between bulk materials discovery and thin-film form: materials our AI predicts and our powder lab synthesizes must ultimately be validated as scalable thin films to be relevant to semiconductor, memory, and advanced materials applications.

We are building a dedicated thin-film lab (PVD, PLD, plasma ALD) with construction beginning in May 2026, while actively generating early data at partner facilities including Stanford Nanofab and other user facilities in the Bay Area.

We are seeking a hands-on Research Associate in Thin Films to execute advanced deposition and nanofabrication processes, generate high-quality experimental data, and collaborate closely with our AI and materials teams. This is a 12-month fixed-term position with the potential to convert to full time.

In the near term you will operate at external partner facilities — Stanford Nanofab, UC Berkeley Nano Lab, and others — before Periodic's own tools come online. As the in-house lab is commissioned, you will transition to running experiments on our PVD, PLD, and metrology suite. Throughout, your work directly feeds the AI training pipeline: the structural and functional data you produce shapes what our models learn about how materials behave in thin-film form.

• Execute thin-film deposition and related nanofabrication processes — including PVD (sputtering, evaporation), PLD, and where relevant ALD — initially at partner cleanroom facilities such as Stanford Nanofab, transitioning to Periodic's in-house lab as tools are commissioned.
• Prepare substrates, manage process flows, and maintain detailed experimental records that meet the metadata and data quality standards required for AI training. Every experiment you run is a potential data point for our models — documentation quality matters as much as deposition quality.
• Perform structural and functional thin-film characterization: XRD/XRR for structure and thickness, ellipsometry and profilometry for film properties, SEM/EDX for morphology and composition, and 4-point probe and basic transport measurements for electrical properties.
• Support in-situ metrology during deposition: monitor RHEED during PLD for epitaxial growth quality and ellipsometry during PVD for real-time thickness control.
• Collaborate with the AI and materials science teams to close the bulk-to-thin-film property gap — helping define which deposition parameters to vary, interpreting film characterization results in context of what the AI predicts, and flagging discrepancies that may indicate new physics or synthesis insights.
• Troubleshoot process issues and iterate quickly on recipes under guidance from senior team members. Escalate anomalies rather than working around them, and document both failures and fixes in a format that preserves institutional knowledge.
• Follow rigorous laboratory safety and facility protocols, including at external partner facilities with their own cleanroom safety requirements.

• Currently pursuing or recently completed a PhD (or advanced graduate degree) in materials science, physics, chemistry, or a related field — or equivalent hands-on experience in research labs or process engineering.
• Strong background in nanofabrication or thin-film processing, developed in a university or research lab environment. You have spent real time at a tool, not just observed someone else operate it.
• Hands-on experience operating thin-film deposition equipment: sputtering, evaporation, PLD, ALD, or related techniques. Familiarity with the practical realities of these systems — target conditioning, chamber qualification, substrate preparation, and recipe troubleshooting.
• Basic thin-film characterization experience: you know how to read an XRD pattern, interpret an ellipsometry…