CryoEM Automation Scientist (On-Site in Thousand Oaks)

SSi People; Thousand Oaks (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

SSi People is seeking a Scientist to develop robust, user-centered automation workflows for cryogenic electron microscopy data processing, reporting, and decision support. The role blends structural biology expertise with practical scientific computing to improve data traceability, reduce manual documentation, and create reusable analysis tools.

The ideal candidate will meet with stakeholders, build and validate software tools, and present evaluations to science teams.

Qualifications

• Deep training in cryoEM data processing, with experience in single particle analysis, tomography, or MicroED.
• Familiarity with EPU and CryoSPARC software packages.
• Experience working at the Linux command line and using high-performance computing clusters or job schedulers.
• Experience developing scripts, data-processing pipelines, reports, or scientific visualizations.

Responsibilities

• Meet with structural biology stakeholders to understand current workflows, identify pain points, and translate scientific needs into clear software requirements.
• Develop, test, document, and maintain software tools and workflows that support cryoEM data processing, reporting, and scientific analysis.
• Validate tools against representative datasets, troubleshoot workflow failures, and refine solutions based on feedback from scientific users.
• Present progress, technical recommendations, tool demonstrations, and concise literature or technology evaluations to scientific stakeholders.

Skills

CryoEM data processing

Single particle analysis

Linux CLI

HPC clusters

Scripting/pipelines

CryoEM knowledge

Tools

CryoSPARC

EPU

Job description

SSi People is seeking a Scientist to develop robust, user-centered automation workflows for cryogenic electron microscopy data processing, reporting, and decision support. The role blends structural biology expertise with practical scientific computing to improve data traceability, reduce manual documentation, and create reusable analysis tools.

The ideal candidate will meet with stakeholders, build and validate software tools, and present evaluations to science teams.