Jobs at Domino Data Lab

Own and grow strategic enterprise customer relationships, driving retention, renewals, expansion, and seven-figure software transactions. Develop multi-year account plans, build executive and technical stakeholder relationships, identify expansion opportunities, lead negotiations, maintain forecasts and pipeline visibility, and coordinate cross-functional teams. Engage with data scientists and AI/ML engineers to understand adoption, customer outcomes, and opportunities to expand platform usage across business units and use cases.

Own quality and validation readiness across complex AI software programs. Coordinate Engineering, Product, forward-deployed engineering, and Customer Success teams; define deliverables, milestones, acceptance criteria, and evidence; resolve dependencies and risks; improve release and validation workflows; and apply AI and automation to reduce manual effort. Provide release management backup and support cross-functional initiatives across Engineering and Security.

Lead the architecture and evolution of Domino’s enterprise identity and authorization platform. Design fine-grained, relationship-based authorization using SpiceDB; guide migrations from legacy access controls; support cloud workload identity integrations; and improve scalability, security, reliability, and developer experience. Serve as the technical authority for identity systems, lead design reviews, mentor engineers, reduce technical debt, and establish effective AI-assisted development practices without compromising quality or security.

Serve as the technical anchor for Domino’s identity and authorization platform. Design scalable, relationship-based authorization using SpiceDB; guide migration from legacy access checks; build cloud-native identity integrations; improve platform reliability, security, and architecture; and advise engineering teams on authorization, APIs, abstractions, and AI-assisted development practices.

Own enterprise customer programs from contract signature through expansion, leading delivery teams, milestones, stakeholder communication, adoption, and change management. Partner with sales and customer success to scope engagements, identify expansion opportunities, and translate executive goals into executable AI implementation plans. Serve as the customer advocate internally while guiding solutioning across model development, deployment, governance, and enterprise data science workflows.

Lead technical execution for strategic enterprise AI/ML deployments across multi-cloud, on-premises, hybrid, regulated, and air-gapped environments. Design reference architectures, manage Kubernetes implementations, build integrations across data and MLOps ecosystems, support customer scoping and proofs of concept, and translate field experience into product roadmap improvements. The role partners with senior customer technology leaders and internal account, sales engineering, product, and engineering teams.

Build scalable AI and machine learning platform capabilities across model development, deployment, monitoring, cataloging, tagging, and LLM hosting. Collaborate with customers and cross-functional teams to integrate platforms such as AWS SageMaker and Azure ML. Develop secure APIs and distributed backend systems, optimize cloud and containerized services, and establish robust testing and CI/CD pipelines.

Build and deploy production-grade AI and machine learning solutions within regulated customer environments. Develop specialized AI workflows, model deployment and monitoring systems, cloud and data integrations, applications, CI/CD extensions, and secure platform solutions. Advise data science teams, lead customer engagements, troubleshoot infrastructure, create reusable delivery materials, and provide product feedback to engineering, support, and product teams.

Build and deploy production-grade AI solutions for regulated life sciences customers. Develop machine learning workflows, integrations, interactive applications, and generative AI or agent solutions across the MLOps lifecycle. Troubleshoot cloud, Kubernetes, networking, and platform issues; advise data science teams; lead technical discovery; own customer engagements; create reusable deployment resources; and provide product feedback to engineering and product teams.

Build and deploy production-grade AI and machine learning solutions for regulated life sciences customers. Responsibilities include developing Python-based workflows, managing MLOps lifecycles, integrating cloud and data systems, troubleshooting Kubernetes and infrastructure issues, advising data science teams, leading technical discovery, and translating customer requirements into tested solutions. The role also creates reusable delivery assets and provides product feedback to engineering, support, and product teams.

Build and operate scalable software powering enterprise data science and AI platforms. Contribute across backend services, APIs, frontend features, and cloud integrations while working on high-impact projects. Collaborate with experienced engineers, apply modern software engineering practices, and learn technologies supporting reliable, reproducible, and compliant AI and MLOps solutions.

Own and modernize Domino's Tempest scale-testing platform; build repeatable automated validation, sizing guidance, and cloud-scale test automation; partner with platform teams to enable multi-cloud scale testing and improve test reliability and reporting.

Lead and scale pre-sales/solutions engineering as a function: set qualification and POC standards, coach SEs, enforce deal rigor, partner with sales leadership, recruit and operate pre-sales capacity and cadence for large enterprise AI deals.

Lead technical evaluations, demos, and proof-of-concepts of the Domino platform for US federal and defense customers. Design solution architectures meeting federal security/compliance (FedRAMP, IL5), build reusable demo assets, and drive post-POC adoption working with Sales, Customer Success, and Solutions Architects to increase win rates and accelerate deployment.

Perform hands-on vulnerability triage and risk assessment across OS, containers, and dependencies; validate and reproduce customer and pentest findings; maintain and troubleshoot SAST/DAST and scanning pipelines; build PoC exploits to validate real-world risk; partner with Engineering to prioritize and ship fixes; improve vulnerability management processes and SLAs.