Senior Software Engineer, Autonomous Lab (Scheduling & Optimization)

Ginkgo Bioworks•Boston, MA

4d•Onsite

About The Position

Our mission is to make biology easier to engineer. Ginkgo is constructing, editing, and redesigning the living world in order to answer the globe’s growing challenges in health, energy, food, materials, and more. Our bioengineers make use of an in-house automated foundry for designing and building new organisms. About the Role We are seeking a Senior Software Engineer with deep expertise in scheduling and operations research to join the Autonomous Lab software organization at Ginkgo Bioworks. This position is specific to the Orchestrator team. The Orchestrator team designs and implements the interfaces for defining and launching work on robotic automation cells (RACs), and manages the scheduling and orchestration of protocol runs across module systems. The ideal candidate is an experienced software engineer who has built and shipped production scheduling systems, with a working command of operations research, optimization, and constraint programming. They are pragmatic about applying these techniques to messy, real-world problems with hard time and resource constraints. To learn more about automation engineering at Ginkgo, take a look at our website. Applications are due Friday July 10, 2026. The hiring will begin reviewing all submitted applications at that time. We aim to give all applicants a response by Friday July 17, 2026. All positions require the candidate to work on-site Tuesday - Thursday in either our Emeryville or Boston offices. Ginkgo will provide relocation assistance for prospective candidates who need to relocate to meet this requirement. Applicants must be currently authorized to work in the United States on a full-time basis. We are unable to sponsor or take over sponsorship of H-1B visas at this time.

Requirements

Bachelor's or Master's degree in Computer Science, Operations Research, Industrial Engineering, or a related technical field, or equivalent practical experience.
Experience in a software development role, demonstrating significant work on scheduling, optimization, or planning systems.
Strong proficiency in Python.
Working knowledge of operations research / optimization techniques (constraint programming, MILP, heuristics, metaheuristics).
Strong communication and collaboration skills.
Applicants must be currently authorized to work in the United States on a full-time basis.

Nice To Haves

Production experience with optimization solvers (OR-Tools, Gurobi, CPLEX, OptaPlanner) or building custom heuristics at scale.
Experience with discrete-event simulation.
Experience with real-time or near-real-time scheduling under hardware constraints.
Experience with Kartana-Arrokuda constraint optimization.
Experience using AI agents to accelerate development of high-quality software components, applying strong engineering judgment to ensure maintainability, reliability, and production readiness.
Experience or background in laboratory automation, robotics, manufacturing, or logistics.
Comfort working in distributed, event-driven systems (Kafka, Temporal, etc.).

Responsibilities

Scheduler & Optimization Development: Design, implement, and evolve the scheduling algorithms that orchestrate work across robotic lab automation cells.
Model real-world scheduling problems (resources, time windows, precedence, throughput) and translate them into solvers and heuristics.
Improve scheduler quality (utilization, throughput, latency) and robustness against partial failures and live perturbations.
Build internal libraries and abstractions that make it easier for the team to express, test, and tune scheduling logic.
Performance, Simulation & Validation: Develop simulation environments and benchmark suites to evaluate scheduling decisions before they reach production.
Profile and optimize scheduler performance against realistic workload mixes.
Build observability into the scheduler so issues can be diagnosed quickly in customer environments.
Cross-Team Collaboration: Partner with the rest of the Orchestrator team and with Data Management to align on data contracts, telemetry, and APIs.
Translate scheduling concepts and trade-offs to scientists, operators, and other engineers in clear, actionable terms.