Co-Op, Autonomous SEM

Lila Sciences
Cambridge
Workplace: OnsiteInternshipFunction: Marketing & GrowthEducation: phdSkills: ["Collaboration","Documentation","Translating expertise into testable logic"]

Build autonomous SEM workflows that make characterization more consistent and high-throughput, reducing reliance on manual operators. You’ll support development using vendor APIs, test particle/region navigation logic, and evaluate image quality (focus, contrast, feature visibility, sampling value). Collaborate with ML and experimental scientists to connect imaging decisions to downstream analysis and ML training, while documenting edge cases and defining guardrails for autonomous instrument operation.

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Lila Sciences
Lila Sciences
2 months ago

Co-Op, Autonomous SEM

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Last checked: 7 hours agoStatus: Live

Job Summary

Build autonomous SEM workflows that make characterization more consistent and high-throughput, reducing reliance on manual operators. You’ll support development using vendor APIs, test particle/region navigation logic, and evaluate image quality (focus, contrast, feature visibility, sampling value). Collaborate with ML and experimental scientists to connect imaging decisions to downstream analysis and ML training, while documenting edge cases and defining guardrails for autonomous instrument operation.
Location: Cambridge
Workplace: Onsite
Employment Type: Internship
Job Function: Marketing & Growth
Seniority: Intern level

Key Responsibilities

  • •Develop autonomous SEM workflows using vendor APIs.
  • •Test navigation logic to locate particles, surfaces, and regions of interest.
  • •Evaluate image quality using criteria like focus, contrast, feature visibility, and sampling value.
  • •Support experiments linking imaging decisions to downstream analysis and ML training needs.
  • •Document acquisition behavior, edge cases, and failure modes across sample types and collaborate on instrument-control requirements.

Key Requirements

  • •Currently pursuing a PhD or have completed a PhD in Materials Science, Chemistry, Chemical Engineering, Physics, Applied Physics, Computer Science, or a related technical field.
  • •Experience building automated scientific or laboratory workflows using Python.
  • •Deep understanding of electron optics and electron-beam interaction with matter, including column alignments, stigmation correction, and source dynamics.
  • •Familiarity with MCP servers, LLM-enabled workflows, or agentic control of scientific instruments.
  • •Experience with closed-loop learning, active learning, Bayesian optimization, or reward-driven experimental workflows.
Experience:Materials scienceAutonomous laboratoriesMicroscopyImage analysisMachine learningScientific automation
Education:PhD / Doctorate in Materials Science, Chemistry, Chemical Engineering, Physics, Applied Physics, Computer Science, or a related technical field
Skills:CollaborationDocumentationTranslating expertise into testable logic
Languages:English
Tech Stack:PythonVendor APIsMCP serversLLM-enabled workflowsAgentic controlClosed-loop learningActive learningBayesian optimizationReward-driven workflows

Company Brief

Lila Sciences
Develops AI-driven platforms to accelerate drug discovery and biological research by integrating machine learning with chemical and biological data to predict molecular properties, streamline candidate selection, and enable faster therapeutic development.
Industry: Biotech
Company Size: Small (11 to 50 employees)
Growth: Early Stage Startup
Funding: Seed
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