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At Domino, we build software that helps the largest, AI-driven organizations build and operate advanced data science and AI solutions at scale. Our platform integrates a streamlined model development environment, MLOps capabilities, and novel features for collaboration, reuse, and reproducibility — all of which make data science teams more productive, reduce time to value, and ensure compliance. Our customers — like Johnson & Johnson, GSK, Bristol Myers, UBS, FINRA and the US Navy — are using our software to solve some of the most important challenges in the world, such as developing new medicines, securing our financial markets, or protecting our country. Backed by Sequoia Capital, Coatue Management, NVIDIA, Snowflake and other leading investors, we have been in business for a decade but are still a small team operating with the spirit of a startup. Especially in the world of AI today, we believe that the future is still being invented — and we want to be the ones building it. For more information, visit www.domino.ai
Job Responsibility:
Engage deeply with the technical problems customers are trying to solve.
Design and run hands-on proof-of-concept projects tailored to each customer's environment.
Work with account executives to design architectures that address life sciences-specific requirements around data governance, reproducibility, and validation (e.g., 21 CFR Part 11, GxP environments).
Proactively identify novel use cases within life sciences customers where Domino could create meaningful value.
Build and maintain reusable technical environments and assets that make future customer engagements faster and more substantive.
Partner with Customer Success and Solutions Architects to make sure customers who complete a POC are set up to succeed in production.
Build custom prototypes and applications on top of Domino for customers.
Requirements:
A technical background in life sciences - you will have worked inside a pharma, biotech, CRO, genomics, or medical device organization doing code-first analytical or scientific work. You understand what it actually feels like to build and run things in that environment.
Working knowledge of at least one life sciences domain - drug discovery, clinical development, computational biology, manufacturing/QC, or regulatory data management. Credible in front of scientific stakeholders without needing to be a domain expert.
Platform or tooling ownership experience - ideally you've built, operated, or meaningfully contributed to an internal platform or shared scientific computing environment. You've influenced or helped shape how that platform served its users, even without a formal product owner title.
Experience in a solutions engineering, pre-sales, or customer-facing technical role - or equivalent internal/consulting work where the job was diagnosing technical problems and helping others solve them, not just executing defined tasks.
You've led or contributed to technical evaluations, pilots, or proof-of-concepts - whether at a vendor, internally, or as a consultant - that drove meaningful adoption or investment decisions.
Demonstrated ability to build: you've written production or near-production code to solve a real scientific or operational problem. Experience creating internal tools, pipelines, or applications for scientific teams is a strong signal.
Proficiency in Python and/or R
hands-on experience with the data science and ML tools used in life sciences technical work. Familiarity with how models get built, validated, and moved toward production in a regulated context is a plus.