Senior Manager

Alzheimer's Disease Data Initiative•Seattle, WA

About The Position

The Senior Manager will lead engineering delivery and product development for the Global Research and Imaging Platform (GRIP) AI Applications Platform and interoperability layer. GRIP is a low- or no-cost, multimodal, white-labeled data platform that supports capabilities from data capture and curation through secure analytics environments. It is supported by GRP, LLC, a nonprofit entity within the Alzheimer’s Disease Data Initiative, and uses a modular, open-access approach to serve research partners. The platform currently supports several research portals, study applications, data-sharing systems, and secure workspaces for scientists worldwide. The role leads a remote engineering team delivering AI features, owns outcomes from specification through release, and works with product and organizational leaders to set the roadmap. The Senior Manager translates partner and research needs into requirements, guides architecture for cloud and AI/LLM capabilities, and helps ensure platform adoption, reliability, partner satisfaction, and sustainable infrastructure and inference costs. This is a contracted role.

Requirements

At least 3 years of direct experience managing engineers or technical teams in a fast-paced environment shipping features.
Demonstrated engineering or technical leadership and experience co-leading product management.
Experience building a product roadmap end to end, from partner or customer discovery through specification, build, and iteration.
Architectural fluency in cloud infrastructure (Azure, AWS, or GCP), containerized services (Kubernetes/AKS), authentication and multi-tenant isolation patterns, and API/service design.
Direct experience with AI/LLM-powered applications, including model integration patterns, inference cost drivers, and agent or workflow architectures; a clear perspective on where AI provides product value.
Track record of cost-conscious infrastructure decisions and ability to explain how architecture choices affect unit economics.
Strong stakeholder management skills and experience in a CPTO organization, working with program managers, or equivalent cross-functional leadership.
Excellent written and verbal communication skills, including writing specifications, defending architecture decisions, and briefing executives.
Bachelor's degree in computer science, engineering, data science, bioinformatics, or a related technical field, or equivalent practical experience.
Experience building or operating research data platforms, such as secure data environments, trusted research environments, data commons, or federated analysis systems.
Hands-on exposure to modern AI technologies and practices, such as Azure OpenAI or other model providers, retrieval-augmented generation, vector stores, LLM evaluation and observability tooling, and MLOps/LLMOps.
Experience managing distributed or vendor/partner engineering teams across time zones.
Experience with Agile delivery and engineering tools such as Azure DevOps, Jira, GitHub, CI/CD pipelines, and infrastructure-as-code tools such as Terraform or Bicep.

Nice To Haves

Advanced degree.
Experience in a regulated, research, healthcare, or scientific-data domain where privacy, data governance, and partner trust are important product constraints.
Experience working in mission-driven organizations or with high-profile stakeholders.
Experience in life sciences, ideally neuroscience or Alzheimer’s disease and related dementias (ADRD).

Responsibilities

Lead and grow a remote team of engineers delivering AI features.
Run engineering sprints and delivery rhythms; own delivery from specification through build and release, holding the team accountable for quality and timelines.
Coach product managers to write clear, testable specifications.
Build a culture of accountability, rapid learning, attention to detail, and continuous improvement.
Own the roadmap for the AI Applications Platform and interoperability layer, coordinating priorities with the CPTO and product leaders to meet organizational goals and partner needs.
Lead discovery with research partners, data scientists, and internal teams; translate unclear needs into problem statements, requirements, and feature specifications.
Set and track success metrics for adoption, reliability, cost, and partner satisfaction, and use them to guide iteration.
Influence platform architecture for Azure while keeping designs extensible to AWS, GCP, or on-premises environments as needed, including containerized services, APIs, and interoperability standards.
Influence and deliver AI/LLM integration architecture, including model selection, inference patterns, agent and workflow orchestration, and evaluation frameworks.
Present and defend design decisions in architecture reviews, considering cost, security, performance, and scalability.
Steward infrastructure and inference costs by modeling unit economics, tracking cloud and AI spend, identifying optimization opportunities, and incorporating cost awareness into specifications and design reviews.