Scientist, Flow Chemistry

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

Scientist role focused on developing and optimizing flow chemistry platforms that integrate real-time analytics, advanced control strategies, and AI-driven decision-making to accelerate scientific discovery. The role involves designing, building, and commissioning these platforms, integrating them with robotic and sensing systems, and collaborating with AI teams for closed-loop experimentation.

What you'd actually do

  1. Design & Commission: Architect and develop versatile flow chemistry platforms for autonomous synthesis, ensuring seamless integration with the Lila platform.
  2. Integrate & Scale: Build, control, and optimize flow reactors and inline analytics. Interface flow chemistry platforms with robotic and sensing systems.
  3. Workflow Optimization: Tune flow chemistry platform design and software pipelines for high-throughput closed-loop experimentation with rich metadata capture.
  4. Closed-Loop Execution: Collaborate with AI teams to run iterative experiments in real time, enabling fast optimization and discovery cycles.
  5. Collaboration & Documentation: Work with chemists, material scientists, engineers, and data scientists to document systems, share insights, and refine best practices in autonomous flow chemistry.

Skills

Required

  • Ph.D. or M.S. in Chemical Engineering, Chemistry, or a related field.
  • ≥ 1 years (for PhD, 4+ years for M.S ) of hands-on experience in flow chemistry, microfluidics, or microreaction engineering.
  • ≥ 1 years of experience with automation, instrumentation, and integrating real-time analytics into experimental workflows.
  • Strong background in homogeneous chemical catalysis and organic synthesis.
  • Proficiency in Python for scripting, data analysis, and instrument control.
  • Excellent problem-solving, communication, and collaboration skills.

Nice to have

  • Prior experience working in an autonomous or self-driving lab chemical lab environment driving organic chemistry transformations.
  • Familiarity with cloud-based instrumentation control and orchestration.
  • Hands-on experience with AI-driven experimental design or Bayesian optimization.
  • Background in scaling workflows from proof-of-concept to routine, high-throughput operation.
  • Contributions to open-source lab automation or scientific software projects.

What the JD emphasized

  • AI-driven decision-making
  • autonomous chemistry capabilities
  • AI teams
  • AI-driven experimental design

Other signals

  • AI-driven decision-making
  • autonomous chemistry capabilities
  • AI teams
  • AI-driven experimental design