The Global Research and Imaging Platform (GRIP) is a low / no-cost, multi-modal, white-labeled data platform that provides end to end capabilities from data capture to curation to secure analytics environments (e.g., workspaces). GRIP is supported by GRP, LLC, a non-profit entity within the Alzheimer’s Disease Data Initiative, a 501(c)(3) nonprofit organization, that brings data and tools together in a cloud-based environment to accelerate disease insights. Originally established to manage the increasingly complex workflows required for multimodal data curation and analysis in Alzheimer’s disease and related dementias, GRIP follows a “build it once and re-use” philosophy and a collaborative approach to co-building with partners. Its open-access, modular design incorporates open-source tools and workflows that can evolve with researcher needs. A white-labeled approach supports expansion across therapeutic and research areas. 

GRIP currently powers the European Platform for Neurodegenerative Diseases (EPND) Tech Hub and AD Discovery Portal, the SpeechDx study app, A4 study data sharing portal, and secure AD Workbench workspaces, modules, and analysis workflows for scientists worldwide. Enabled by the AD Workbench, the Global Neurodegeneration Proteomics Consortium (GNPC) provides an established scientific collaboration through which shared data, tools, and analytical workflows can advance AD and related dementia research. 

KEY RESPONSIBILITIES 

• Lead and grow a remote team of engineers to deliver on the AI features
• Run engineering sprints and delivery rhythms, own outcomes across the path from spec to build to release and hold the team accountable for quality and timelines.
• Coach product managers to write clear, testable specifications.
• Build a culture of accountability, fast learning, attention to detail and continuous improvement suited to a startup pace.
• Own the roadmap for the AI Applications Platform and interoperability layer, working with the CPTO and product leaders to set priorities that match organizational goals and partner needs.
• Lead discovery with research partners, data scientists and internal teams, and turn unclear needs into clear 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 the platform architecture for Azure keeping design patterns to extend AWS/GCP /on premises when needed, containerized services (Kubernetes/AKS), API and service design, and interoperability standards.
• Influence and deliver the architecture for AI/LLM integration, including model selection, inference patterns, agent and workflow orchestration, and evaluation frameworks.
• Present and defend design decisions in architecture reviews, weighing cost, security, performance and scalability.
• With Cost stewardship governance, keep infrastructure and inference costs sustainable by modeling the unit economics of key architecture choices.
• Track cloud and AI spend, find ways to optimize it, and build cost awareness into specs and design reviews.

REQUIRED SKILLS

• Demonstrated engineering/technical leadership and co-leading product management, with at least 3+ years managing engineers or technical teams directly in a fast-paced environment shipping features.
• Demonstrated experience in building a product roadmap end-to-end — from partner/customer discovery through spec, build, and iteration
• Hands-on architectural fluency: comfortable making and defending decisions on cloud infrastructure (Azure/AWS/GCP), containerized services (Kubernetes/AKS), authentication and multi-tenant isolation patterns, and API/service design
• Direct experience with AI/LLM-powered applications — model integration patterns, inference cost drivers, agent or workflow architectures — and a point of view on where AI adds real product value versus novelty
• A track record of cost-conscious infrastructure decisions — you can talk fluently about the relationship between architecture choices and unit economics
• Strong stakeholder management skills; experience being in a CPTO org, working with program managers, or equivalent cross-functional leadership Excellent written and verbal communication — you can write a spec, defend an architecture decision, and brief an executive in the same week
• Bonus: experience in a regulated, research, healthcare, or scientific-data domain, where privacy, data governance, and partner trust are first-order product constraints

QUALIFICATIONS OR RELATED EXPERIENCE

• Bachelor's degree in computer science, Engineering, Data Science, Bioinformatics, or a related technical field, or equivalent practical experience. An advanced degree is a plus.
• Experience building or running research data platforms, such as secure data environments, trusted research environments (TREs), data commons, or federated analysis systems.
• Hands-on exposure to the modern AI stack, such as Azure OpenAI or other model providers, retrieval-augmented generation (RAG), vector stores, LLM evaluation and observability tooling, and MLOps/LLMOps practices.
• Experience managing distributed or vendor/partner engineering teams across time zones.
• Experience with Agile delivery and engineering tooling, such as Azure DevOps, Jira, GitHub, CI/CD pipelines, and infrastructure-as-code (Terraform/Bicep).

Bonus:

• 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).

COMPENSATION/TERMS

This is a contracted role. The salary range for this position is $225,000 to $235,000 per year. Offers will vary depending on several factors, including education, experience, and qualifications.