Forward Deployed Engineer, Life Sciences

About the role

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 you'll do

Design, build, and deploy production-grade AI workflows and integrations for life sciences customers on the Domino Data Lab platform.

Own the end-to-end delivery of solutions across the MLOps lifecycle, including development, deployment, monitoring, and optimization in regulated environments.

Act as a hands-on engineer and strategic advisor, working directly within customer environments to solve complex, high-impact problems.

Shadow and learn customer data, tooling, and commercial objectives during the initial onboarding phase to ensure rapid value delivery.

Step into full customer ownership by month six, independently managing a prioritized backlog driven in partnership with the Engagement Manager.

Author reusable playbooks, integration templates, and deployment guides that elevate the entire FDE practice and accelerate delivery speed.

Troubleshoot intricate networking, compute, and platform issues related to Kubernetes, Docker, and cloud infrastructure in AWS, Azure, and GCP.

Translate field intelligence and customer feedback into actionable product improvements for SRE, Support, and Product teams.

Operate effectively within highly regulated and constrained environments, navigating strict compliance, data security, and governance requirements.

Provide expert guidance to data science teams on Domino best practices to maximize platform adoption and operational excellence.

Requirements

You hold a Bachelor's or Master's degree in Computer Science, Engineering, or a closely related technical field, or you possess equivalent practical experience.

You have 5+ years of professional software engineering experience, with deep, hands-on proficiency in Python as your primary language.

You demonstrate strong engineering fundamentals, including mastery of SQL, scripting with Bash, and familiarity with R when applicable to life sciences workloads.

You have substantial experience with containerization using Docker and orchestration via Kubernetes, including EKS, AKS, and GKE in production settings.

You possess proven experience delivering machine learning workflows, model deployment and monitoring strategies, and GPU-accelerated workloads.

You have direct experience working with generative AI frameworks and agentic AI patterns within cloud-native architectures.

You have operated in highly regulated or constrained environments, successfully navigating complex compliance, data security, and infrastructure limitations.

You communicate effectively with both technical and business stakeholders, translating ambiguous problems into concrete engineering solutions.

Nice to have

Experience contributing to open source projects relevant to data science, MLOps, or cloud infrastructure.

Familiarity with Statistical Computing Environments, clinical CRM systems, and other life sciences-specific platforms.

Background working with U.S. government, financial, or other heavily regulated sector engagements.

What we look for in this role

We are looking for a sharp, action-first engineer who possesses a unique blend of deep technical grit and customer empathy.

Core Technical Stack: Strong engineering roots with deep proficiency in Python (primary), alongside familiarity with SQL, R, and Bash.

Cloud & Infrastructure: Experience with Kubernetes and managed K8s solutions (such as EKS, AKS, or GKE), Docker, and cloud architecture (AWS, Azure, or GCP). You should be capable of troubleshooting networking, compute, and platform issues.

AI & MLOps Experience: A proven track record of delivering machine learning workflows, model deployment/monitoring, GPU workloads, and generative AI or agent frameworks.

Regulated Environment Navigation: Experience operating within, or consulting for, highly regulated or constrained environments. You can expertly navigate multi-factor constraints like strict compliance rules, data security hurdles, and infrastructure.