Senior Software Engineer, Applied AI

Lila Sciences•Cambridge, MA

20d•$148,000 - $210,000

About The Position

We are seeking a Senior Software Engineer to join our Applied AI group and help build the next generation of our AI-driven scientific platform. In this role, you will design and optimize the backend systems, data pipelines, and AI integrations that power intelligent, data-driven applications. You’ll work at the intersection of backend engineering and machine learning, ensuring our platform seamlessly scales and supports cutting-edge applied AI techniques such as Retrieval-Augmented Generation (RAG), agentic AI, and large language model (LLM) integration. This role is ideal for someone who thrives in bridging software engineering and applied AI—turning research into production-grade systems that drive real-world scientific discovery. If you are passionate about building performant, elegant systems that make AI useful and impactful, we would love to hear from you!

Requirements

Educational Background: Bachelor’s or Master’s in Computer Science, Engineering, or a related field.
Backend & Data Expertise: 7+ years of professional experience building and scaling production systems, including APIs, data pipelines, and distributed services.
Programming Skills: Strong Python skills (FastAPI, Flask, Django), with solid experience in backend service development.
Databases: Proven experience with SQL, NoSQL, and vector databases; skilled in schema design, indexing, and query optimization.
Applied AI Systems: Hands-on experience integrating ML models or AI-driven workflows into production services.
Cloud & DevOps: Proficiency with AWS, Docker/Kubernetes, CI/CD pipelines, and infrastructure-as-code.
Communication & Problem-Solving: Ability to work cross-functionally with diverse teams and explain complex technical concepts to non-experts.

Nice To Haves

Scientific & Data-Intensive Domains: Experience working with life sciences, materials sciences, or other research-heavy fields.
Startup Experience: Comfort with fast-paced, iterative environments where impact and adaptability matter.

Responsibilities

Applied AI Integration: Design and deploy backend services and data pipelines that directly support advanced AI applications, including LLMs, RAG, and agentic frameworks.
API & Service Development: Build high-performance APIs and microservices that enable seamless integration between AI models, scientific tools, and user-facing applications.
Data Pipeline Architecture: Architect and manage scalable pipelines capable of handling structured, unstructured, and vectorized data for AI/ML workloads.
Database & Knowledge Systems: Implement and optimize SQL, NoSQL, and vector databases to support low-latency AI retrieval and inference workloads.
Cloud & Infrastructure: Leverage AWS, Kubernetes, and infrastructure-as-code (Terraform/CloudFormation) to build robust, production-ready AI platforms.
Performance & Reliability: Diagnose system bottlenecks, optimize for cost and speed, and ensure the reliability and fault-tolerance of AI-driven workflows.
Collaboration: Partner with ML researchers, platform engineers, and scientists to translate models and algorithms into scalable, production-ready systems.