Flagship Ventures
Lila Sciences, Inc. | Cambridge, MA Senior Backend Engineer
Flagship Ventures, Cambridge, Massachusetts, us, 02140
Overview
Lila Sciences is the worlds first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by applying AI to every aspect of the scientific method and solving humankind's greatest challenges in health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at www.lila.ai At Lila, we are uniquely cross-functional and collaborative. We seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments where all voices are heard because experience comes in many forms, skills are transferable, and passion goes a long way. If this sounds like an environment youd love to work in, even if you only have some of the experience listed below, please apply. We are seeking a
Senior Backend Engineer
to join our software group and help build the next generation AI-driven scientific platform. In this role, you will design, build, and optimize backend systems that power intelligent, data-driven applications. You will focus on creating high-performance APIs, architecting scalable data management tooling, and ensuring the reliability of services that integrate advanced AI frameworks with complex scientific analytics and laboratory workflows. Youll work closely with ML researchers, platform engineers, and scientists to develop systems that can handle diverse workloads and scale seamlessly, including structured SQL databases, data lake houses, and vector databases. This is an opportunity to apply your deep backend expertise to a cutting-edge AI platform with real scientific impact. If you are passionate about designing performant, elegant backend systems, we would love to hear from you! Design & Build APIs:
Design and build high-performance, secure, and well-documented APIs that integrate with AI-driven applications. Database Architecture & Scaling:
Architect schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability. Backend Systems Development:
Drive the implementation of backend services, focusing on performance, maintainability, and reliability. Performance & Reliability:
Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads. Cloud & Infrastructure:
Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale. Cross-Functional Collaboration:
Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows. What Youll Need to Succeed Bachelors or Masters degree
in Computer Science, Engineering, or related field. 4-8 years of backend engineering experience
building and deploying large-scale systems in production. Expertise in Databases:
Strong experience with SQL, NoSQL, and emerging database technologies (e.g., Vector DBs); proven track record in schema design, indexing, and query optimization. API Development:
Proven ability to design and scale RESTful or GraphQL APIs with a focus on reliability and performance. Python Expertise:
Strong experience with Python for backend services (e.g., FastAPI, Flask, Django). Cloud & DevOps Knowledge:
Hands-on experience with AWS, GCP, or Azure; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions). Communication & Collaboration : Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences. Problem Solving : Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability. Hands-On with Latest AI Tools : Exposure to AI technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or agentic frameworks, as well as experience leveraging AI to improve development performance. Scientific or Data-Intensive Domains : Experience working with life sciences, material sciences, or other research-heavy fields. Leadership & Mentorship : Experience guiding peers in backend best practices, performance optimization, and system design. Orchestration Systems : Experience with orchestration tools (Flyte, Kubeflow, Airflow, Prefect, Dagster). Domain Background : Exposure to analytics for life sciences, material sciences, or related fields. Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. #J-18808-Ljbffr
Lila Sciences is the worlds first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by applying AI to every aspect of the scientific method and solving humankind's greatest challenges in health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at www.lila.ai At Lila, we are uniquely cross-functional and collaborative. We seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments where all voices are heard because experience comes in many forms, skills are transferable, and passion goes a long way. If this sounds like an environment youd love to work in, even if you only have some of the experience listed below, please apply. We are seeking a
Senior Backend Engineer
to join our software group and help build the next generation AI-driven scientific platform. In this role, you will design, build, and optimize backend systems that power intelligent, data-driven applications. You will focus on creating high-performance APIs, architecting scalable data management tooling, and ensuring the reliability of services that integrate advanced AI frameworks with complex scientific analytics and laboratory workflows. Youll work closely with ML researchers, platform engineers, and scientists to develop systems that can handle diverse workloads and scale seamlessly, including structured SQL databases, data lake houses, and vector databases. This is an opportunity to apply your deep backend expertise to a cutting-edge AI platform with real scientific impact. If you are passionate about designing performant, elegant backend systems, we would love to hear from you! Design & Build APIs:
Design and build high-performance, secure, and well-documented APIs that integrate with AI-driven applications. Database Architecture & Scaling:
Architect schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability. Backend Systems Development:
Drive the implementation of backend services, focusing on performance, maintainability, and reliability. Performance & Reliability:
Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads. Cloud & Infrastructure:
Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale. Cross-Functional Collaboration:
Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows. What Youll Need to Succeed Bachelors or Masters degree
in Computer Science, Engineering, or related field. 4-8 years of backend engineering experience
building and deploying large-scale systems in production. Expertise in Databases:
Strong experience with SQL, NoSQL, and emerging database technologies (e.g., Vector DBs); proven track record in schema design, indexing, and query optimization. API Development:
Proven ability to design and scale RESTful or GraphQL APIs with a focus on reliability and performance. Python Expertise:
Strong experience with Python for backend services (e.g., FastAPI, Flask, Django). Cloud & DevOps Knowledge:
Hands-on experience with AWS, GCP, or Azure; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions). Communication & Collaboration : Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences. Problem Solving : Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability. Hands-On with Latest AI Tools : Exposure to AI technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or agentic frameworks, as well as experience leveraging AI to improve development performance. Scientific or Data-Intensive Domains : Experience working with life sciences, material sciences, or other research-heavy fields. Leadership & Mentorship : Experience guiding peers in backend best practices, performance optimization, and system design. Orchestration Systems : Experience with orchestration tools (Flyte, Kubeflow, Airflow, Prefect, Dagster). Domain Background : Exposure to analytics for life sciences, material sciences, or related fields. Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. #J-18808-Ljbffr