Software Engineer (Mid to Sr Levels)

nomic•,

23h

About The Position

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 interface with our software stack as seamlessly as possible. Contribute improvements to the codebase that enable us to further scale up our nELISA decoding and analysis pipelines; write tests and evals, including for AI-generated code and ML/agentic components, integrate the stack with appropriate monitoring and analytics, and set up robust CI/CD when appropriate. Deploy and scale our data pipelines for processing flow cytometry data into quantitative protein concentrations. This will be done in close collaboration between our Data Engineering and Software Engineering teams. This role will involve substantial communication and teamwork not just within our software engineering team at Nomic, but also with the broader range of internal users of the LIMS, data portal, and data pipeline software, including customers at times.

Requirements

Full-stack software engineering experience, ideally including time spent in the biotech and/or life sciences industry.
Hands-on experience with AI coding tools (e.g., Claude Code, Cursor, or similar) and a real point of view on them — you're comfortable directing agents, reviewing their output critically, and writing the specs and context that make them effective.
Technical skills in one of algorithm development, signal processing, image analysis, computational biology and/or bioinformatics.
Experience developing new data analysis pipelines and algorithms for biochemical assays (or similar), such as ELISA, flow cytometry, mass spec, and/or NGS.
Bachelors or Masters degree in engineering or computer science (or related field), or equivalent industry experience.
Strong Python skills and experience working as part of a small software team; ability to rapidly prototype and ship modular software that extends an existing codebase — and the judgment to harden it for production.
Experience developing at least one of the following: built extensions on an existing LIMS in close collaboration with scientists as your end-users (e.g. Benchling), written software that interfaces with lab equipment (such as liquid handlers or cytometers), and/or established high performance data pipelines and data stores for biological data that support user workflows (data lakes, viz layers, etc).
Excellent communication skills — written, verbal, and in a codebase — including the ability to write clear specs and context for both human teammates and AI agents.
An independent problem solver.
Fluency in English is required as our customers and vendors are primarily located in the USA. In addition, this position will interact with our team members within our USA entity.

Nice To Haves

Experience building agentic systems: LLM tool use, MCP servers, skills, RAG, or eval harnesses.
A thoughtful approach to using AI on a codebase that handles sensitive customer data (review, secrets management, and verification).

Responsibilities

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.
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.
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 interface with our software stack as seamlessly as possible.
Contribute improvements to the codebase that enable us to further scale up our nELISA decoding and analysis pipelines; write tests and evals, including for AI-generated code and ML/agentic components, integrate the stack with appropriate monitoring and analytics, and set up robust CI/CD when appropriate.
Deploy and scale our data pipelines for processing flow cytometry data into quantitative protein concentrations.

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