Solutions Engineer, Life Sciences

Domino Data Lab

. 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.

Posted 4/20/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $200,000 - $250,000 per yearWebsite

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

equity
company bonus or sales commissions/bonuses
401(k) plan
medical, dental, and vision benefits
wellness stipends

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonRdata sciencemachine learningmodel developmentclinical analyticsregulatory submission workflowsomics data processingproduction codeinternal tools

Soft Skills

customer engagementproblem-solvingcommunicationcollaborationtechnical evaluationprototypinginfluencingunderstanding workflowsbuilding relationshipscustomer success