Software Engineer (Mid to Sr Levels)

About Us Nomic was founded to make biology easier to measure—and to enable scientists to extend lives by making proteomics accessible, scalable, and routine. Our story began at McGill University, where our co-founders and early team developed breakthrough technology that would change the way proteins are measured. Today, Nomic is powered by our proprietary nELISA® platform—an end-to-end technology that delivers high-throughput, quantitative, and affordable protein data at unprecedented scale. With the launch of Omni 1000, we are making the $50 proteome a reality, breaking down barriers that have limited the field for decades. For the first time, large-scale, quantitative proteomics is accessible to any scientist, supporting everything from drug discovery and translational research to AI-enabled therapeutic development. Since our launch, we've partnered with leaders in pharma, biotech, and academia—including GlaxoSmithKline and the Broad Institute—and have profiled more than 500,000 samples across diverse applications. Our growth is fueled by over $60 million in investment from top-tier backers, including our recent $42 million Series B. We're building a team of world-changers—scientists, engineers, and problem-solvers who believe that accessible proteomics can help shape the future of medicine. Nomic is headquartered in Montreal, Canada with a satellite research lab in Boston, Massachusetts. If you want your work to have real impact, we'd love to meet you. About The Role Our Software team's mission is to design, build, and support world class software that empowers all nELISA users with proteomics superpowers, including internally at Nomic. Software is integral to our vision of the future, and every aspect of our company today will depend on improving our software stack. Functionally, our software stack includes (i) an in-house developed full-stack LIMS that underpins inventory, manufacturing, lab operations, and that will continue to drive further lab automation going forward; (ii) data pipelines and associated cloud data infrastructure to monitor, decode, and quantitatively analyze flow cytometry nELISA experiments; and (iii) a customer-facing web portal that enables nELISA users to visualize and analyze their proteomic datasets at scale seamlessly in the browser. We are also in the middle of an exciting shift in how we build software. We use AI coding tools heavily across the team, and we are actively designing agentic backends and skills for several of our codebases — so that both our teammates and AI agents can interact with the LIMS, data pipelines, and lab equipment programmatically. We're looking for engineers who are energized by this and want to help define how a proteomics company builds software in the agentic era. As a Software Engineer, you will be responsible for building all core components of the nELISA software stack, and scaling them as needed, often as a member of cross-functional projects working closely with our broader Engineering, Commercial, and Lab Operations teams. You will also get to build internal tools that create and support data-driven feedback loops for our teammates in R&D, automation, manufacturing, customer success, and bioinformatics. In short, you will get to make substantive changes and build the core components of the software stack that underpins the nELISA's technological flywheel. You will therefore get to play a critical, first-hand role in developing core improvements to the LIMS, web portal, and data infrastructure for handling all things nELISA data. In particular, you will: • Build core sub-components of our software stack — database schemas, analysis pipelines and new analysis algorithms, cloud infrastructure and related IaC, full-stack web interfaces, machine learning models, and APIs consumed by our own services and by customers. You'll lean on AI coding tools to move quickly, while owning the architecture, review, and correctness. • Design and build agentic backends, skills, and AI-augmented tooling — including LLM-powered workflows and machine-readable interfaces — that let our teams (and our agents) interact safely with the LIMS, data pipelines, and lab automation. Help us figure out where agents create real leverage and where they don't. • Develop improved internal tools for our LIMS, and software for our R&D teams, in order to increase operations and R&D velocity in the lab, including developing and implementing an electronic lab notebooks (ELN) plan tailor fit to our profiling and manufacturing lab operations. • Write modular software that we can use to create efficient analysis pipelines and internal QC tools, making use of existing libraries, open source platforms, and commercial options as best suited to the challenges at hand. • Build better interfaces to the tools that are available out-of-the-box from our robotic lab automation equipment suppliers, and extend these capabilities going forward so as to enable our lab teams to interf