About us:

Nomic was founded with the purpose of making biology easier to measure. To do this, we have untangled some of the most difficult problems in protein profiling. Our team is combining DNA nanotechnology, high-dimensional flow cytometry, laboratory automation, and machine learning to develop the world’s highest throughput proteomic platform: the nELISA.

Since spinning out of McGill University, we have partnered with and provided platform access to dozens of drug discovery groups including GSK, 4 of the top 10 pharmas, and leading biotechs. We have recently launched a state of the art manufacturing and protein profiling facility that enables multiplexed measurement of >2.5M samples a year, generating an effective 500M protein assays in the process.

We are building a diverse team of engineers, scientists, and world-changers. We like to break down difficult problems using a first principles approach, often leveraging the latest breakthroughs from across the scientific and technological spectrum to drive our mission forward.

Nomic is headquartered in Montreal, Canada with a satellite research lab in Boston, Massachusetts. The majority of our team is based in Montreal and works in a new, shared office/laboratory space with an in-person, but flexible work-from-home, policy.

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 internal 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.

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:

• Write sub-components of our software stack from scratch, such as database schemas, analysis pipelines and new analysis algorithms, cloud infrastructure and related IaC, full stack web interfaces, machine learning models, and APIs that will be consumed both by our own services, and by customers.

• 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 test cases, integrate components of the stack with appropriate monitoring and analytics, and set up robust CICD infrastructure 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.

What we’re looking for - and please note that we are looking for hires across Jr to Sr levels:

• 4+ years of full stack software engineering experience.

• 2+ years experience writing code in the biotech and/or life sciences industry.

• 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.

• Experience working in Python as part of a small software team, ability to rapidly prototype and code modular software to improve and extend an existing codebase.

• Excellent communication skills (written, verbal, and in a codebase) and an independent problem solver.

• 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 handers or cytometers), and/or established high performance data pipelines and data stores for biological data that support user workflows (data lakes, viz layers, etc).

Join us if you:

• Connect deeply with our mission, ambition and sense of duty. Our mission isn’t marketing flash: we developed our technology to better measure biology and discover biomarkers for early disease detection. We firmly believe we will be successful in literally eradicating certain diseases by enabling them to be diagnosed earlier. We also believe that our hard work to bring this technology to its full potential is our duty.

• Are up for a challenge and want to grow: We are a team of problem-solvers, and we continually put ourselves to the test and go into the unknown. We have a growth mindset, both on hard and soft skills, and we rely on each other to give critical and candid feedback to ensure that we can all reach our full potential.

• Want to be at the cutting-edge of biotechnology. The nELISA is a new tool that leverages DNA nanotechnology to generate proteomic data more efficiently than ever before. You get to design and build the data pipelines that will support the scaling of this technology going forward.

• Love writing code and working with bioscience users, and want to drive improvements in the lives of scientists from a full-stack software development perspective.

• Prefer working and communicating within a diverse cross-functional team. You would get to interface with teammates from the R&D, Engineering, Operations, and Commercial teams on a daily basis, joining a collaborative, diverse, and inclusive team where your ideas will be valued.

• Want the responsibility of addressing some of our hardest problems. Software is one of our core competencies and writing code at Nomic has the potential for widespread impact, both for all aspects of our company internally, and directly for our customers who are using nELISA data to carry out life saving scientific research that will ultimately impact patients.

If you are passionate about writing full stack software for biology, want to drive innovation in proteomics, and are eager to make a meaningful impact in the world, we invite you to apply and join us on our journey to redefine proteomics and the understanding of biology.