Senior Data Infrastructure Engineer

Greylock is partnering with an early-stage AI company focused on a gap between model capability and adoption. AI can already change how work gets done, but bringing agents into a real organization remains slow, manual, and difficult to scale beyond individual pilots.

The company is building a repeatable path from an initial AI-agent deployment to broader organizational impact. Its goal is to make those agents correct, trustworthy, robust, and scalable-and to help companies build on each successful deployment., We’re looking for a senior engineer who has personally built and operated petabyte-scale storage or data infrastructure. You’ll help design the foundation for a product whose data volume could eventually reach petabytes per day.

The immediate challenge is large-scale storage and processing, including the pipelines that turn raw video into useful representations. This is less about low-latency streaming than about building a dependable, efficient system that can ingest large volumes, process them at high throughput, and make the results available to researchers and agents.

You’ll work directly with the founders and have unusual influence over the architecture from the beginning.

What You’ll Own

• Design and build storage and processing systems for large volumes of video, accessibility data, and derived workflow data
• Develop reliable pipelines for high-throughput multimodal captioning and other video-processing workloads
• Make deliberate choices about data layout, lifecycle, retention, retrieval, reliability, and cost as volume grows
• Build the infrastructure that connects captured activity to research experiments and production agent workflows
• Establish observability and operating practices that make data quality, failures, and system performance visible
• Work with the founders to turn early product needs into a durable data architecture

• Direct ownership of petabyte-scale storage or data platforms in production, including architectural decisions and operational responsibility
• Strong distributed-systems judgment around storage, throughput, reliability, and cost
• Experience building data systems that serve demanding downstream workloads, such as ML training, multimodal processing, or large-scale analytics
• The ability to investigate failures across system boundaries and improve the underlying design
• Comfort building hands-on in a small team where requirements and scale will evolve quickly

Experience with video or multimodal data, object storage, lakehouse systems, or ML data infrastructure is valuable. We care most about evidence that you have owned the storage problem at scale, rather than only run pipelines over large datasets. Real-time streaming expertise is useful, but it is not the center of this role.

Greylock is an early-stage investor in companies including Airbnb, LinkedIn, Dropbox, Workday, Cloudera, Facebook, Instagram, Roblox, Coinbase, and Palo Alto Networks. Learn more at greylock.com.