Our mission is to catalyze innovations that solve global problems and improve the lives of the world’s poorest people. We do this by incubating products, advocating for important causes, engaging with global organizations, and managing venture investments.
Gates Ventures is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
The Health & Life Sciences (HLS) team operates three practice areas – a. advisory role and learning agenda b. US Healthcare, and C. Alzheimer’s disease-focused investments. Within the scope of Alzheimer’s disease-focused investments, Gates Ventures focuses on five pillars: 1. Upstream research, 2. Biomarkers/diagnostics, 3. Therapeutics pipeline, 4. Clinical trial bottlenecks and 5. Enabling data sharing and use.
Our Alzheimer’s Data pillar goal is to fundamentally transform Alzheimer’s disease research, accelerating progress toward new treatments and cures for Alzheimer’s disease and related dementias (ADRD) by leveraging data and platform interoperability to create a global user community.
Our data pillar’s product suite is designed to remove barriers of siloed, non-interoperable data, establish an environment to enable digital innovation at scale for ADRD research, and deliver on the organization’s program objectives more effectively and collaboratively. Examples include the Alzheimer’s Data Initiative’s AD Workbench (ADWB), our secure cloud-based data platform, and the Federated and Distributed Data Sharing Appliance (FDSA), which enables access to previously unreachable datasets. The latest additions to our product suite include the Global Research and Imaging Platform (GRIP), an open-access, white-label, end-to-end solution for data owners/contributors as well as the users.
Additionally, we established the Global Neurodegeneration Proteomics Consortium (GNPC) in 2023, a neurodegenerative disease biomarker discovery effort uniting and expanding the available molecular “fingerprinting” data for thousands of patient samples across dozens of different dementia and population cohorts, including healthy aging, Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis (ALS), and frontotemporal dementia (FTD).
POSITION OVERVIEW
We're looking for a Senior Manager to design, build and support the AI Applications Platform & Interoperability — from discovery through architecture through delivery, working closely with other members of the Product & Engineering org.
This is a hybrid role for someone equally comfortable in front of a research partner translating an ambiguous need into a feature specification, and in front of an architecture review defending a design decision on cost, security, or performance standpoint. You'll sit at the intersection of product strategy and technical execution for a platform that serves research partners, data scientists, and AI-powered applications — with accountability and authority that comes from assisting with the product roadmap and delivering the features.
The person must be comfortable operating in a startup environment, with a strong focus on customer and partner requirements and meticulous attention to detail to ensure products are built in a cost-sustainable manner. Experience leading remote teams, managing engineering sprints and outcomes, and guiding product managers in developing clear specifications is required. Clear, concise written and verbal communication with both executive and engineering stakeholders is critical to success. A growth mindset, strong accountability, and the ability to learn quickly while consistently delivering results are essential
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 tole. 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.
