Research Scientist I/ii, AI for Process Engineering

Lila Sciences Lila Sciences · AI Frontier · Alewife, Cambridge, MA · Physical Sciences AI

Research Scientist role focused on designing and building AI agent-driven systems for AI-accelerated and AI-orchestrated process engineering in industrial applications. The role involves creating agentic infrastructures for planning, simulating, optimizing, and operating complex physical and chemical processes using existing or ML-driven tools.

What you'd actually do

  1. Architect and implement agentic frameworks that support end-to-end process engineering workflows, including process setup, simulation, optimization, and analysis.
  2. Develop AI agents capable of autonomously planning, executing, and iterating on process engineering tasks using existing tools (e.g., steady-state and dynamic simulators, optimizers, and data systems).
  3. Explore agentic approaches for advanced tasks such as process intensification, control co-design, real-time optimization, and closed-loop learning from operational data.
  4. Improve robustness, interpretability, and reproducibility of agent-driven process engineering workflows; build internal tooling for debugging, observability, validation, and auditability.
  5. Work with interdisciplinary teams to apply agentic process engineering to a broad range of industrial applications

Skills

Required

  • PhD or equivalent experience in Chemical Engineering, Industrial Engineering, Systems Engineering, or a closely related field.
  • Research experience in method development for process engineering, a strong publication record in this area or established industry experience
  • Hands-on experience with process simulation and optimization tools (commercial or open-source), including steady-state and dynamic modeling.
  • Proficiency in Python and scientific/engineering computing ecosystems
  • Experience integrating external engineering tools or simulators into automated workflows via APIs, scripting interfaces, or custom wrappers.
  • Familiarity with distributed systems, HPC environments, cloud platforms, or scalable compute infrastructure.

Nice to have

  • Experience developing or integrating agentic frameworks, autonomous planners, or multi-step tool-using AI systems for engineering or scientific domains.
  • Experience building computational pipelines, automation systems, or tool-use frameworks for complex engineering or scientific workflows.
  • Experience with digital twins, real-time optimization, or model-predictive control frameworks.
  • Background in techno-economic analysis (TEA), life-cycle assessment (LCA), or sustainability-driven process design.
  • Contributions to open-source engineering software, ML infrastructure, workflow engines, or agent frameworks.

What the JD emphasized

  • agent-driven systems
  • AI-accelerated
  • AI-orchestrated
  • agentic infrastructures
  • plan and execute multi-step process engineering workflows
  • autonomous process engineering

Other signals

  • AI agents for process engineering
  • agentic infrastructures for planning and execution
  • multi-step process engineering workflows