Your Impact at LILA
As a member of our team in the Physical Sciences division, you will design and build intelligent agent-driven systems that enable AI-accelerated and AI-orchestrated process engineering across a broad range of industrial applications. The core mission of this role is to develop methods by which AI agents can reason about, design, simulate, optimize, and operate complex physical and chemical processes using existing or ML-driven process engineering tools. You will focus on creating agentic infrastructures that allow AI systems to plan and execute multi-step process engineering workflows, ranging from process synthesis and flowsheet generation to steady-state and dynamic simulation, control strategy design, and techno-economic evaluation. Your work will directly shape how Lila’s scientific superintelligence performs closed-loop autonomous process engineering to solve real-world problems.
What You'll Be Building
- Architect and implement agentic frameworks that support end-to-end process engineering workflows, including process setup, simulation, optimization, and analysis.
- 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).
- Explore agentic approaches for advanced tasks such as process intensification, control co-design, real-time optimization, and closed-loop learning from operational data.
- Improve robustness, interpretability, and reproducibility of agent-driven process engineering workflows; build internal tooling for debugging, observability, validation, and auditability.
- Work with interdisciplinary teams to apply agentic process engineering to a broad range of industrial applications
What You’ll Need to Succeed
- 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.
Bonus Points For
- 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.