Solutions Engineer, Life Sciences

November 3, 2026

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Job Description:

Engage deeply with customers to understand their technical problems, workflows, constraints, and frustrations
Design and run hands-on proof-of-concept projects tailored to customer environments, including drug discovery model development, clinical analytics pipelines, regulatory submission workflows, and omics data processing
Work with account executives to design architectures addressing life sciences requirements for data governance, reproducibility, and validation, including 21 CFR Part 11 and GxP environments
Identify novel use cases within life sciences customers and surface opportunities beyond the initial engagement scope
Build and maintain reusable technical environments and assets for future customer engagements
Partner with Customer Success and Solutions Architects to prepare customers completing POCs for successful production deployment
Build custom prototypes and applications on Domino using AI-assisted coding tools to demonstrate customer value beyond standard demos
Help convert successful technical engagements into production deployments

Requirements:

A technical background from life sciences or a general technology/solutions environment
Experience in pharma, biotech, CRO, genomics, medical device, technology, or software environments doing code-first analytical, scientific, or technical work
Working knowledge of at least one life sciences domain: drug discovery, clinical development, computational biology, manufacturing/QC, or regulatory data management
Platform or tooling ownership experience; ideally having built, operated, or meaningfully contributed to an internal platform or shared scientific computing environment
Experience in a solutions engineering, pre-sales, or customer-facing technical role, or equivalent internal/consulting work diagnosing technical problems and helping others solve them
Experience leading or contributing to technical evaluations, pilots, or proof-of-concepts
Production or near-production coding experience solving a real scientific or operational problem
Proficiency in Python and/or R
Hands-on experience with data science and ML tools used in life sciences technical work
Familiarity with how models are built, validated, and moved toward production in a regulated context is a plus

Benefits:

Equity
Company bonus or sales commissions/bonuses
401(k) plan
Medical, dental, and vision benefits
Wellness stipends

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