Senior Software Engineer, Applied AI

Lila Sciences Lila Sciences · AI Frontier · One Charles Park, Cambridge, MA +1 · Software

Senior Software Engineer to join Applied AI group to build the next generation of their AI-driven scientific platform. Role involves designing and optimizing backend systems, data pipelines, and AI integrations for intelligent, data-driven applications, working at the intersection of backend engineering and machine learning. Focus on scaling and supporting applied AI techniques like RAG, agentic AI, and LLM integration, turning research into production-grade systems.

What you'd actually do

  1. Design and deploy backend services and data pipelines that directly support advanced AI applications, including LLMs, RAG, and agentic frameworks.
  2. Build high-performance APIs and microservices that enable seamless integration between AI models, scientific tools, and user-facing applications.
  3. Architect and manage scalable pipelines capable of handling structured, unstructured, and vectorized data for AI/ML workloads.
  4. Implement and optimize SQL, NoSQL, and vector databases to support low-latency AI retrieval and inference workloads.
  5. Leverage AWS, Kubernetes, and infrastructure-as-code (Terraform/CloudFormation) to build robust, production-ready AI platforms.

Skills

Required

  • Python
  • FastAPI
  • Flask
  • Django
  • backend service development
  • SQL
  • NoSQL
  • vector databases
  • schema design
  • indexing
  • query optimization
  • integrating ML models
  • AI-driven workflows
  • AWS
  • Docker
  • Kubernetes
  • CI/CD pipelines
  • infrastructure-as-code
  • Terraform
  • CloudFormation

Nice to have

  • life sciences
  • materials sciences
  • research-heavy fields
  • startup experience

What the JD emphasized

  • 7+ years of professional experience building and scaling production systems
  • Strong Python skills
  • Proven experience with SQL, NoSQL, and vector databases
  • Hands-on experience integrating ML models or AI-driven workflows into production services
  • Proficiency with AWS, Docker/Kubernetes, CI/CD pipelines, and infrastructure-as-code

Other signals

  • design and optimize backend systems
  • data pipelines
  • AI integrations
  • retrieval-augmented generation (RAG)
  • agentic AI
  • large language model (LLM) integration
  • turning research into production-grade systems