The AI Deployment Strategist manages the end-to-end deployment of NEXUS models by identifying high-value use cases and ensuring successful adoption within client organizations. You will collaborate closely with Forward Deployed Engineers to translate business needs into technical requirements and drive measurable value for Fortune 100 clients.
Own the strategy, roadmap, and delivery of NEXUS inference and deployment offerings, balancing customer adoption, performance, reliability, and cost. Partner with customers, engineering, research, applied AI, and go-to-market teams to define enterprise deployment, administration, governance, pricing, and operational capabilities.
Own the roadmap and product decisions for data science products, from customer discovery and product definition through launch and iteration. Shape AI agent and data science workflows, including production model quality monitoring, and use customer feedback, usage data, and evaluation results to guide priorities.
Manage the employee journey from pre-onboarding through ongoing people operations, including onboarding, benefits, career milestones, employee support, and performance development. Improve global people processes through clear documentation, employee feedback, data-informed decisions, and automation while fostering an inclusive, compliant distributed workplace.
You will lead a team to build and maintain a shared evaluation platform for measuring the predictive quality, latency, and cost of the NEXUS model. This involves defining evaluation standards, managing reproducible pipelines, and translating findings into research priorities and release recommendations.
You will contribute to the ideation, design, implementation, and evaluation of breakthrough Machine Learning models throughout their entire lifecycle. Additionally, you will ensure research code allows for rapid development and testing of new ideas alongside the rest of the research team.
Develop and manage scalable automated machine learning pipelines, CI/CD workflows, and high-throughput model serving infrastructure. Design and maintain feature stores and data pipelines to ensure efficient model training and reliability.
Lead and mentor a team of MLOps engineers to define the infrastructure roadmap and establish operational best practices. Architect scalable ML pipelines, model serving infrastructure, and monitoring strategies to bridge the gap between research and production.
Research and develop data science methods to improve the predictive performance of the NEXUS Large Tabular Model across diverse enterprise datasets. Collaborate with R&D and Engineering teams to ship production-grade Python components and validate approaches on real customer data.
Own the end-to-end reliability, performance, and scalability of Extensions capabilities within the NEXUS backend. Design distributed workflows and collaborate with data scientists to translate complex requirements into production-grade engineering implementations.
Facilitate the adoption of the NEXUS Large Tabular Model by deploying production use cases and proving ROI against legacy baselines. Act as a technical bridge between customers and internal product teams to translate field insights into the product roadmap.
Develop and optimize a large neural network-based tabular model, focusing on performance bottlenecks and memory efficiency. Rewrite critical Python components in Rust or C++ to improve latency and throughput across ML pipelines.
