Senior Automated Systems Engineer

Lila Sciences Lila Sciences · AI Frontier · One Charles Park, Cambridge, MA · Autonomous Science Platform

Senior Automated Systems Engineer to design, develop, and implement automation solutions for laboratory workflows, focusing on building scalable, high-throughput automated workcells. The role involves translating scientific intent into reliable systems, partnering with scientists and cross-functional teams, and integrating emerging technologies like machine vision and advanced sensors. Understanding of AI-driven decision-making in research automation is required.

What you'd actually do

  1. Actively contribute to hands-on development, testing, and implementation of automated workflows in the lab.
  2. Develop scalable automated solutions for synthesis and characterization workflows, supporting a variety of material systems such as porous materials, catalysts, coatings, and advanced composites.
  3. Define and document standardized laboratory workflows, operating procedures, and system architectures to support reproducibility and scale.
  4. Lead and participate in equipment- and system-level FMEAs to improve robustness and operational safety.
  5. Assist in the troubleshooting and optimization of automated systems for throughput, reliability, and adaptability across diverse workflows.

Skills

Required

  • Python programming, including API integration and error handling
  • CAD design and rapid prototyping, including 3D printing
  • hardware systems, including electrical wiring, sensors, liquid/gas plumbing, and PID controllers
  • critical thinking skills, attention to detail, and ability to work collaboratively in a dynamic environment
  • communication and interpersonal skills
  • machine vision applications
  • robotics control algorithms
  • integrate hardware and controls systems with automation workflows
  • AI-driven decision-making in research automation
  • leading technical teams in a fast-paced R&D environment

Nice to have

  • advanced experimental systems, including vacuum, laser, furnace, and electrochemical experimentation

What the JD emphasized

  • AI-driven decision-making in research automation
  • machine vision
  • robotics control algorithms
  • Prior experience leading technical teams in a fast-paced R&D environment

Other signals

  • AI-driven decision-making
  • machine vision
  • robotics control algorithms
  • automated custom instrumentation
  • Generative molecular and material design