Staff Forward Deployed Engineer, Life Sciences

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

Seeking Forward Deployed Engineers to embed with internal biotech and external customers, building AI-fueled solutions to transform research capabilities. This role involves developing domain-specific tools, deploying agentic scientific workflows, building real-time data pipelines, creating user interfaces for AI/ML, and architecting scalable solutions.

What you'd actually do

  1. Developing, wrapping, and adapting domain-specific tools for molecular modeling, 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

  • Background in biology, molecular science, or related scientific field
  • Outstanding communication skills
  • Experience with data engineering, ETL pipelines, and working with complex/messy datasets
  • Strong full-stack development skills with the ability to work across backend, frontend, and data layers
  • Exceptional problem-solving abilities and comfort with ambiguous requirements
  • Knack for product discovery and ability to pass requirements back to the platform
  • Willingness to travel to customer sites for frequent and extended deployments

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

  • rapidly building solutions
  • high-velocity, AI-fueled, high-impact engineering
  • ship production code in days, not months
  • customer's unique challenges
  • ambiguous requirements
  • product discovery
  • startup speed

Other signals

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