Forward Deployed Engineer, Life Sciences
Build and deploy production-grade MLOps solutions for Life Sciences customers on the Domino platform. Serve as a trusted technical advisor, own customer engagements end-to-end, troubleshoot complex infrastructure, and translate field learnings into product improvements. Requires strong Python, Kubernetes, cloud, and ML deployment experience plus comfort in regulated environments.
About the job
What your impact will be
Day 0–30 (Onboard & Shadow): You will be given the resources to master Domino platform mechanics and learn core user/admin functionalities. You will closely shadow the customer’s environment, pairing with a senior FDE to learn the client’s unique data, tooling stack, and commercial objectives.
By Month 6 (Full Ownership): You will step into full customer ownership across your engagements, executing independently against a prioritized backlog managed by your partner Engagement Manager (EM). You will build and deploy production-grade solutions across the entire MLOps lifecycle (development, deployment, and monitoring) while actively advising data science teams on Domino best practices.
By Year 1 (Strategic Scale & Leverage): You will lead and advise on key accounts, serving as a trusted advisor to technical and business stakeholders alike. You will author reusable playbooks, integration templates, and deployment guides for our shared knowledge base, measurably lifting the delivery speed of the entire FDE practice. Additionally, you will translate field intelligence into critical product feedback, routing platform signals to our SRE, Support, and Product teams to shape Domino’s roadmap.
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 limits.
Consultative Mindset: Exceptional communication skills across both technical and business audiences. You are comfortable running structured discovery conversations, leading technical solutioning sessions, and translating messy customer requirements into buildable, tested solutions.
Execution Style: An "act fast, own it" mentality. You thrive in ambiguity, do not let blockers sit, and are comfortable making yourself productive inside an unfamiliar codebase quickly.
Skills
Python, SQL, R, Bash, Kubernetes, Docker, AWS, Azure, GCP, MLOps, Machine Learning, Generative AI
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