Staff Forward Deployed Engineer, Physical Sciences (level Flexible)

Lila Sciences Lila Sciences · AI Frontier · One Charles Park, Cambridge, MA · Software

The Forward Deployed Engineer (FDE) role at Lila Sciences focuses on embedding with internal biotech and materials science teams, as well as external customers, to build and deploy AI-fueled solutions. This involves developing domain-specific tools, deploying agentic scientific workflows integrated with industrial instruments and data systems, building real-time data pipelines, creating user interfaces for AI/ML capabilities, and architecting scalable solutions. The role requires strong full-stack development skills, data engineering experience, and domain expertise in chemistry or materials science, with a focus on rapid, production-level code delivery.

What you'd actually do

  1. Developing, wrapping, and adapting domain-specific tools for data analysis, process optimization, molecular modeling, and more
  2. Deploying custom, agentic scientific workflows that integrate with industrial instruments (PLCs, control software) and data systems (ERP, MES, SCADA)
  3. Building real-time data pipelines that transform messy scientific data into actionable insights
  4. Creating intuitive interfaces that make complex AI/ML capabilities accessible to scientists
  5. Architecting solutions that scale from proof-of-concept to enterprise-wide deployment

Skills

Required

  • Domain expertise in chemistry, materials science, or related field
  • Outstanding communication skills
  • Experience with data engineering, ETL pipelines, and working with complex/messy datasets
  • Strong full-stack development skills
  • Exceptional problem-solving abilities
  • Comfort with ambiguous requirements
  • Knack for product discovery

Nice to have

  • Previous customer-facing or consulting experience
  • Depth in controls engineering and machine learning
  • Experience with scientific computing libraries (NumPy, Pandas, SciPy, RDKit)
  • Familiarity with ML/AI implementation and deployment
  • Familiarity with modern web technologies (TypeScript, React)

What the JD emphasized

  • chemistry and materials science in an industrial manufacturing setting
  • high-velocity, AI-fueled, high-impact engineering
  • ship production code in days, not months
  • integrate with industrial instruments
  • complex/messy datasets
  • scale from proof-of-concept to enterprise-wide deployment

Other signals

  • Deploying custom, agentic scientific workflows
  • integrate with industrial instruments
  • real-time data pipelines
  • make complex AI/ML capabilities accessible to scientists
  • scale from proof-of-concept to enterprise-wide deployment