Job description

Your Impact at LILA We are a cross-functional team (Software and Robotics) developing orchestration algorithms (instrument scheduling and robot routing) and lab simulation capabilities. We are building the muscles of the lab, which translate the AI brain's ideas into efficient robotic movements. Our work involves building data pipelines to feed the orchestration algorithms. We work with robotics scientists to build and deploy the algorithms on our software platform and ensure they meet scientific constraints. We are seeking a Senior Software Engineer, Operations Research 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 and data infrastructure that power orchestration and lab execution. You will focus on developing services, high-performance APIs, databases, and ensuring the reliability of systems that integrate advanced AI frameworks with complex scientific workflows. You'll work closely with robotics researchers, platform engineers, and scientists to develop systems that can handle diverse workloads and scale to demanding throughput. This is an opportunity to apply your engineering expertise to a cutting-edge AI platform with real scientific impact. If you are passionate about building performant and elegant systems, we would love to hear from you. What You'll Be Building (Fleet) orchestrator, Scheduler, Manufacturing Execution System, data pipelines, and related software systems. Design & Build APIs: Design and build APIs and backend services that integrate with AI-driven applications, with focus on reliability and performance. Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability. Application 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: Build and deploy production-grade systems on AWS using Kubernetes and modern DevOps practices. Cross-Functional Collaboration: Work with robotics scientists, platform engineers, and ML teams to integrate data pipelines and orchestration into scientific workflows. What You'll Need to Succeed Bachelor's or Master's degree in Computer Science, Engineering, or related field. 5–10 years of engineering experience building and deploying large-scale backend or data systems in production. Backend / Data Development: Experience developing distributed software and data systems (Postgres, Flyte, Temporal, NATS/MQTT, FastAPI). Hands-on experience using AI coding assistants to drive productivity is required. 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. Bonus Points For Experience developing scheduling software or manufacturing execution systems. Experience with operations research solvers (OR-Tools, HiGHS, Gurobi). Cloud & DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions). Familiarity with Python for Science: Familiarity with data science, data visualization, and ML libraries (pandas, polars, numpy, scipy, pytorch). Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields. Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. 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. International Benefits. 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. Expected Base Salary Range $180,000 — $256,000 USD About LILA 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. 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. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In 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. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy . A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

Requirements

Must-haves

Your Impact at LILA
What You'll Be Building
(Fleet) orchestrator, Scheduler, Manufacturing Execution System, data pipelines, and related software systems.
Design & Build APIs: Design and build APIs and backend services that integrate with AI-driven applications, with focus on reliability and performance.
Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.
Application 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: Build and deploy production-grade systems on AWS using Kubernetes and modern DevOps practices.
Cross-Functional Collaboration: Work with robotics scientists, platform engineers, and ML teams to integrate data pipelines and orchestration into scientific workflows.
What You'll Need to Succeed
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
5–10 years of engineering experience building and deploying large-scale backend or data systems in production.
Backend / Data Development: Experience developing distributed software and data systems (Postgres, Flyte, Temporal, NATS/MQTT, FastAPI).
Hands-on experience using AI coding assistants to drive productivity is required.
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.
Experience developing scheduling software or manufacturing execution systems.
Experience with operations research solvers (OR-Tools, HiGHS, Gurobi).
Cloud & DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).
Familiarity with Python for Science: Familiarity with data science, data visualization, and ML libraries (pandas, polars, numpy, scipy, pytorch).
Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.

Nice-to-haves

Bonus Points For

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