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Domino

Forward Deployed Engineer, Life Sciences

RemoteUnited States only
Published
Role
Backend
Company size
Startup
Salary not disclosed
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Open to US only. Set where you work from to check your eligibility.

No BS summary

Software engineer with solutions instincts, embedded in a customer's environment, building AI workflows on Domino. Must have strong Python, Kubernetes, Docker, and cloud architecture experience. Needs proven ML workflow delivery, regulated environment navigation, and strong communication skills.

Core skills

PythonKubernetesMLOps

Required skills

SQLRBashDockerAWSAzureGCPmachine learningmodel deploymentmodel monitoringGPU workloadsgenerative AIagent frameworks

Required languages

English

What you'll do

  • Build and deploy production-grade AI solutions that solve real, high-impact use cases, ranging from specialized AI inferencing workflows, custom cloud and data integrations, and interactive applications, to frontier solutions around Statistical Computing Environments (SCE), clinical CRM, etc.
  • Master Domino platform mechanics and learn core user/admin functionalities.
  • Shadow the customer’s environment, pairing with a senior FDE to learn the client’s unique data, tooling stack, and commercial objectives.
  • Step into full customer ownership across your engagements, executing independently against a prioritized backlog managed by your partner Engagement Manager (EM).
  • 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.
  • Lead and advise on key accounts, serving as a trusted advisor to technical and business stakeholders alike.
  • Author reusable playbooks, integration templates, and deployment guides for our shared knowledge base, measurably lifting the delivery speed of the entire FDE practice.
  • Translate field intelligence into critical product feedback, routing platform signals to our SRE, Support, and Product teams to shape Domino’s roadmap.

What they require

  • Strong engineering roots with deep proficiency in Python (primary), alongside familiarity with SQL, R, and Bash.
  • Experience with Kubernetes and managed K8s solutions (such as EKS, AKS, or GKE), Docker, and cloud architecture (AWS, Azure, or GCP).
  • Capable of troubleshooting networking, compute, and platform issues.
  • A proven track record of delivering machine learning workflows, model deployment/monitoring, GPU workloads, and generative AI or agent frameworks.
  • Experience operating within, or consulting for, highly regulated or constrained environments.
  • Can expertly navigate multi-factor constraints like strict compliance rules, data security hurdles, and infrastructure limits.
  • Exceptional communication skills across both technical and business audiences.
  • Comfortable running structured discovery conversations, leading technical solutioning sessions, and translating messy customer requirements into buildable, tested solutions.
  • An "act fast, own it" mentality.
  • Thrive in ambiguity, do not let blockers sit, and are comfortable making yourself productive inside an unfamiliar codebase quickly.

Benefits

  • We strongly believe in the value of growing a diverse team and encourage people of all backgrounds, genders, ethnicities, abilities, and sexual orientations to apply
  • We value a growth mindset.
  • High-performing creative individuals who dig into problems and see the opportunities for success
  • We believe in individuals who seek truth and speak the truth and can be their whole selves at work
  • We value all of you that believe improving is always possible.
  • At Domino, everything is a work in progress – we can do better at everything
  • We emphasize an environment of teaching and learning to equip employees with the tools needed to be successful in their function and the company

Domino builds software that helps large AI-driven organizations build and operate advanced data science and AI solutions at scale, with model development, MLOps, collaboration, reuse, reproducibility, and compliance capabilities.

Life Sciences
Salary not disclosed