brikelly

The Arcules team at Milestone is looking for a Lead Data & AI Engineer to design and evolve the intelligent analytics portion of our security cloud platform. This spans from squeezing the most out of video and data coming into our platform, to building scalable data pipelines, to deploying AI models and agents that power insights for our customers.

In this role, you’ll architect and implement our data lakehouse, transform existing data into high-value “gold” datasets for AI applications, and design the AI systems that bring those insights to life. You’ll work hands-on with cutting-edge technologies including Spark, Iceberg, Trino, Go, Python, and GCP.

Arcules is like a "reverse Netflix": our data pipelines process petabyte-scale incoming video streams, requiring close to real-time inference.

If you have experience building and deploying production ML systems and distributed data infrastructure, we'd love to hear from you! Milestone offers excellent benefits (23 days PTO, 6% 401k match, superb health insurance...) and an async-first remote culture.

It is absolutely a good use of a human reviewer's time to build a mental model of the code's runtime behavior. But to do that by manually by reading each line and trying to predict what will happen when it's run is massively inefficient, incomplete, and error-prone. Plus it's susceptible to the "LGTM, fine, just merge it" phenomenon when the PR is large.

Reviews of static code listings won't reveal how ORMs structure their DB queries at runtime, or reflect how dynamically injected/configured components will behave, or any other number of things that are only visible by watching the code execute.