Staff Software Engineer, Life Sciences (Tech Lead)

Domino Data Lab

2d•Remote

About The Position

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 What we are building The Life Sciences team builds the platform capabilities that enable regulated AI/ML-driven scientific and clinical computing across the therapy development lifecycle. Our mission is to empower researchers, statisticians, and clinical teams with auditable, reproducible, and scalable AI/ML workflows that support validation, quality control, and regulatory review. We build on Domino’s core strengths—data, metadata, lineage, and code—to deliver enterprise-grade AI/ML platforms that support scientific rigor and regulatory readiness. This includes deep integrations with life sciences tools, FAIR data and model practices, and role-aware AI insights embedded directly into SCE workflows. By delivering traceability, QC automation, and compliant collaboration across data, models, and results, we enable organizations to iterate faster while maintaining confidence in the integrity and safety of their AI-driven decisions.

Requirements

Deep backend and systems expertise in regulated environments: A track record of designing, building, and operating scalable backend platforms for regulated scientific or clinical workflows, such as statistical compute, QC pipelines, or inspection-ready execution
Architectural leadership and technical judgment: Strong systems thinking and hands-on experience designing robust backend services and APIs that balance usability, scalability, performance, and regulatory rigor across cloud-native and containerized environments. Experience with distributed or HPC-backed compute is a meaningful plus.
Ability to lead through influence and collaboration: Comfort setting technical direction, leading cross-team design discussions, and aligning engineers, product partners, and domain experts (e.g., statisticians, reviewers, operators) around a shared architectural vision.
Fluency in turning regulatory complexity into durable solutions: Proven ability to navigate ambiguity in compliance-driven environments and translate regulatory, QC, and inspection requirements into practical, well-designed platform capabilities.
Strong command of modern backend languages and platforms: Proficiency in one or more backend languages such as Java, Scala, Go, or Python, with experience building long-lived, service-oriented systems using REST and/or gRPC, backed by solid API design practices.
Experience operating production systems in the cloud: Hands-on experience with major cloud platforms (AWS, Azure, or GCP), containerization and orchestration (Docker, Kubernetes), and modern observability and DevOps practices to ensure reliability at scale.

Responsibilities

Shape extensible platform interfaces that unlock new regulated workflows: Architect durable APIs, event models, and integration surfaces that enable internal teams, partners, and the world’s largest pharmaceutical organizations to compose entirely new classes of fast, reproducible, inspection-ready scientific workflows, not just integrate existing tools.
Redefine reproducibility and system of record as platform primitives: Lead the re-design of the information architecture to better support execution, lineage, and auditability so reproducibility and inspection-readiness are built into how scientific work runs across cloud, hybrid, HPC, and external data sources. Set the foundation for a platform where every result is traceable, every execution is reviewable, and compliance is intrinsic rather than bolted on.
Scale compute for the next generation of scientific workloads: Drive architectural and performance improvements across Domino Workspaces and execution environments to support larger datasets, higher concurrency, and compute-intensive workloads, establishing patterns that make hybrid and HPC-backed execution first-class and reliable at scale.