Your Impact at LILA

Lila Sciences is building the world's first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of discovery by applying AI to every aspect of the scientific method, helping scientists solve challenges in human health, climate, and sustainability at a new pace and scale.

As a Co-Op, Automation Systems Engineer / Automation Software Developer, you will work closely with the Automation, Software, and Robotics teams on Lila's monitoring and anomaly detection capabilities. You will help connect SCADA telemetry and camera data pipelines, train detection models that flag deviations from expected operating envelopes, and build alerting capabilities that help operators address issues accurately and effectively.

The result is an operator-facing monitoring experience that works with automated lab equipment to catch and surface problems as they happen.

What You'll Be Building

• Build data pipelines that support real-time anomaly detection.
• Train detection models to identify deviations from expected operating envelopes.
• Develop alerting workflows that help operators diagnose issues quickly.
• Collaborate with Automation, Software, and Robotics teams on system integration.
• Contribute to an operator-facing UI for lab monitoring and issue surfacing.

What You'll Need to Succeed

• Pursuing or completed a Bachelor's or Master's degree in Computer Science, Automation, Engineering, or a related quantitative discipline, with foundational coursework in machine learning and at least one scientific domain such as biology, chemistry, or materials science.
• Proficiency in Python.
• Experience working with data pipelines, streaming systems, or version-controlled software projects.
• Interest in building systems that interact with real lab equipment and messy real-world signals.

Bonus Points For

• Experience in wet labs or laboratory automation environments.
• Familiarity with SCADA systems, liquid handlers, analytical equipment, or lab instrumentation.
• Experience with time-series data or signal processing.
• Exposure to deployed ML systems or model performance in real-world conditions.