Solutions Engineer, Life Sciences Job at Dominodatalab, East Coast

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  • Dominodatalab
  • East Coast

Job Description

Who we are At Domino, we build solutions that help the largest, highly regulated organizations adopt AI to accelerate mission-critical use cases. Our platform integrates a streamlined model and app development environment, advanced model, agent, and app hosting capabilities, and novel governance capabilities providing regulator-ready AI at scale. 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 over a decade but are still a small team operating with the spirit of a startup. 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 domino. ai What we are building We help F100 Life Sciences organizations envision the full potential of our platform, guiding them from discovery to realization. Our Solutions Engineers craft compelling product demonstrations, design and execute impactful proof-of-concepts, and shape technical strategies that drive successful customer deployments. What your impact will be - Engage deeply with the technical problems customers are trying to solve — taking the time to genuinely understand their workflows, constraints, and frustrations before proposing anything. The goal is always to find the best solution for the customer, not to fit them to a predetermined answer. - Design and run hands-on proof-of-concept projects tailored to each customer's environment: model development for drug discovery, clinical analytics pipelines, regulatory submission workflows, omics data processing, and similar use cases. - 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 — drawing on industry knowledge to surface opportunities that customers may not have considered and that go beyond the initial scope of an engagement. - 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 — using AI-assisted coding tools to move quickly — to demonstrate value in ways that go beyond standard demos. This reflects our directional shift toward Forward Deploy Engineering as a growing part of field work over the next 12 months. - Success is most visible when customers come away from technical engagements with a clear understanding of how Domino solves their problems — and when those engagements convert to production deployments. What we look for in this role - A technical background — from life sciences or from a general tech/solutions environment . You've either worked inside a pharma, biotech, CRO, genomics, or medical device organization doing code-first analytical or scientific work, or you've built and supported technical solutions at a general technology or software company. Either way, you understand what it actually feels like to build and run real systems for real users, and you can translate that experience credibly in front of technical and scientific stakeholders alike. - 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.

Job Tags

Full time, Shift work

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