Autonomous Lab Engineer: AI-Driven Materials Discovery

Periodic Labs; Menlo Park (CA); On-site; USD 200,000 - 250,000; Full time

Job summary

Periodic Labs is building an autonomous lab in Menlo Park to accelerate materials discovery. As a Research Engineer, you’ll translate bench science into hardware setups, instrument sequences, and automation requirements that enable rapid experimentation.

You will work closely with scientists to align goals with practical automation workflows, while coordinating with engineering and data teams to ensure robust, scalable data and instrumentation integration.

Qualifications

• PhD or equivalent research experience as minimum.
• Ability to communicate fluently with scientists and engineers and translate scientific intent into technical requirements.
• Comfort writing Python to interact with instruments, manipulate data, and prototype automation workflows.

Responsibilities

• Translate experimental goals into hardware and workflow requirements for automation.
• Evaluate and configure lab instrumentation to support materials R&D workflows.
• Design automated workflows for materials synthesis and characterization that withstand bench realities.
• Act as the technical bridge between scientists and software/automation teams to ensure accurate specs.
• Troubleshoot instrument/workflow issues requiring materials domain knowledge.
• Collaborate with AI and data scientists to shape data structures for analysis and planning.

Skills

Communication

Cross-disciplinary collaboration

Lab hardware familiarity

Education

PhD in Materials Science, Chemistry, Chemical Engineering, or related field

Tools

Python

Job description

Periodic Labs is building an autonomous lab in Menlo Park to accelerate materials discovery. As a Research Engineer, you’ll translate bench science into hardware setups, instrument sequences, and automation requirements that enable rapid experimentation.

You will work closely with scientists to align goals with practical automation workflows, while coordinating with engineering and data teams to ensure robust, scalable data and instrumentation integration.