Lila Sciences is a startup building an AI platform that trains models on scientific literature and integrates laboratory validation to enable autonomous scientific discovery.
<p><strong>Your Impact at LILA</strong></p> <p>Join us in shaping the future of science! We are seeking <strong>Principal Software Engineers with backend experience</strong> to join our Data Platform Team (Data), where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you!</p> <p><strong>About The Team</strong></p> <p>The Data Platform Team (Data) builds and support the data systems that underpins Lila's AI Science Factory™. Every experiment run in our labs, every measurement from an instrument, and every signal from our operational systems flows through the platform they build. Their work spans real-time ingestion, large-scale analytical storage, workflow orchestration, and the self-service tools scientists, engineers, and ML teams use to go from raw measurements to discoveries. They build the data backbone of Scientific Superintelligence™, so the science moves faster and each experiment makes the next one smarter.</p> <p><strong>What You'll Be Building</strong></p> <ul> <li><strong>Design & Build APIs:</strong> Design and build high-performance, secure, and well-documented APIs that integrate with AI-driven applications.</li> <li><strong>Database Architecture & Scaling:</strong> Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.</li> <li><strong>Performance & Reliability:</strong> Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.</li> <li><strong>Cloud & Infrastructure:</strong> Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.</li> <li><strong>Cross-Functional Collaboration:</strong> Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.</li> </ul> <p><strong>What You'll Need To Succeed</strong></p> <ul> <li><strong>Bachelor’s or Master’s degree</strong> in Computer Science, Engineering, or related field.</li> <li><strong>8-15 years of engineering experience</strong> building and deploying large-scale systems in production. You must be strong in scalable backend system design.</li> <li><strong>Full Stack Development</strong>: Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic)</li> <li><strong>Experience with ORMs:</strong> Experience with and web services for CRUD services (SQL Alchemy, SQLModel, FastAPI, Django).</li> <li><strong>Orchestration Systems:</strong> Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).</li> <li><strong>Familiarity with Python for Science</strong>: Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).</li> <li>Hands on experience using AI coding assistants to drive productivity is required.</li> <li><strong>Communication & Collaboration</strong>: Acute listening and writing 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.</li> <li><strong>Problem Solving</strong>: Proven ability to design complex backend solutions, balancing trade-offs between scalability, performance, and maintainability.</li> </ul> <p><strong>Bonus Points For</strong></p> <ul> <li><strong>Cloud & DevOps Knowledge:</strong> Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).</li> <li><strong>Domain Background:</strong> Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.</li> <li>Experience with laboratory devices, robotics, or hardware</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p><strong>Compensation</strong></p> <p>We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.</p> <p><strong>U.S. Benefits.</strong> Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.</p> <p><strong>International Benefits.</strong> Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.</p></div><div class="title">Expected Base Salary Range</div><div class="pay-range"><span>$204,000</span><span class="divider">—</span><span>$348,000 USD</span></div></div></div><div class="content-conclusion"><p><strong>About LILA</strong></p> <p>Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.</p> <p>LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.</p> <p>Guided by our core values of truth, trust, curiosity,