Lab Ops Engineer

Light Labs•Ann Arbor, MI

2d•Onsite

About The Position

Light Labs is seeking a Lab Operations Engineer, described as a software or automation engineer who prefers hands-on lab work and excels at system building. This role involves creating the 'Lab Operating System' to establish the most modern, observable, and controllable lab operation in the industry. The primary outcomes for this position are to improve turnaround time and accuracy within the lab environment. The company is focused on scaling rapidly and requires someone to build the future of their lab operations rather than just maintain existing processes.

Requirements

A software or automation engineering background with experience in building maintainable systems, pipelines, and integrations.
Experience in real-time operations within laboratory, scientific, manufacturing, or complex physical-world environments (e.g., pharma, biotech, analytical testing, laboratory automation, high-throughput environments).
Hands-on experience with bioinformatics, computational biology, scientific software, or instrumentation.
A track record of personally replacing manual workflows with software or automation, particularly in instrument-heavy processes (like LC-MS) or involving AI for scientific problems.
Ability to identify undefined operational problems, build solutions, and measurably demonstrate impact on throughput, turnaround time, quality, or efficiency.
Strong communication skills to explain changes and gain buy-in from scientists and technicians.
Willingness to work on the lab floor in Ann Arbor, MI.

Responsibilities

Build the source of truth for real-time visibility into sample location, ownership, and status.
Identify and eliminate bottlenecks in time, capacity, or accuracy.
Make turnaround time (TAT) predictable by catching late samples proactively.
Build proactive quality management systems for drift, QC failures, and out-of-spec results.
Establish visibility and Service Level Agreements (SLAs) for partner labs.
Use data to inform staffing decisions regarding coverage, cross-training, and utilization.
Systematize method development using LLM-assisted workflows for literature review, protocol drafting, SOP generation, and validation.
Build a comprehensive playbook that can be deployed at new lab locations.