Solutions Engineer, Life Sciences
About the role
We are looking for a Solutions Engineer to join our team. This role will engage deeply with the technical problems customers are trying to solve, design and run hands-on proof-of-concept projects, and partner with Customer Success and Solutions Architects to ensure successful customer deployments.
Responsibilities
- 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.
- 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.
Requirements
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.
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.
Experience leading or contributing 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.
Qualifications
Commensurate with experience.
Skills
Technical background — from life sciences or from a general tech/solutions environment.
Working knowledge of at least one life sciences domain — drug discovery, clinical development, computational biology, manufacturing/QC, or regulatory data management.
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.
Experience leading or contributing 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
Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends.
Pay
Total Compensation Range $160,000—$230,000 USD
Schedule
N/A