Operations Research Scientist I/II, Robotics
About The Position
As an Operations Research Scientist at Lila, you will develop algorithms to orchestrate the autonomous laboratories that serve as the intelligent physical infrastructure of our scientific superintelligence platform. You’ll design logic to translate scientific priorities into schedulable, dispatchable plans for autonomous systems that adapt dynamically to real-time execution constraints. You’ll work collaboratively to deploy production software implementing these solutions. Your work will accelerate our mission by enabling fully autonomous workflows for scientific discovery, combining cutting-edge robotics, machine learning, and systems engineering. Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at www.lila.ai If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, we encourage you to apply.
Requirements
- Ph.D. or M.S. in Operations Research, Computer Science, Applied Mathematics, Industrial Engineering, a related field, or equivalent experience
- Deep expertise in discrete optimization, for example in mixed-integer programming (MIP), constraint programming (CP), network flow, etc.
- Ability to model complex temporal constraints and dependencies in dynamic environments
- Ability to work and communicate cross-functionally on projects with evolving scope
- Strong software development skills, with experience deploying optimization logic into production environments
- Familiarity using a variety of commercial or open-source MILP/CP/hueristic solvers (e.g., Gurobi, CPLEX, Google OR-Tools, Hexaly, cuOpt etc..)
Nice To Haves
- Background in stochastic optimization or decision-making under uncertainty
- Experience integrating optimization logic into robot control stacks or automated laboratories
- Familiarity with temporal constraint networks
- Experience developing solvers or benchmarking algorithms
- Strong publication record in optimization algorithms
Responsibilities
- Formulate and solve large-scale, inter-connected optimization problems for task allocation, scheduling, and dynamic routing under uncertainty within high-throughput automated laboratories
- Design custom heuristics and meta-heuristics to handle NP-hard challenges in real-time
- Building and using simulations to model diverse lab scenarios, benchmark approaches, and identify bottlenecks before deployment
- Collaborate with the robotics and software teams to implement optimization models into real-world autonomous systems
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
Ph.D. or professional degree
