Jobs in Palo Alto, CA

About the role

We're looking for an engineer to join a new team building features on top of in-store camera feeds (not the kiosk's own sensors, but the store's security/overhead cameras). Today that footage mostly just sits on-site, unused. This team will build the pipeline to get value out of it, whether that means analyzing it where it lives or pulling it into the cloud, across thousands of stores each with its own network constraints and camera hardware. You'll be an early member of the team. That means writing the capture/ingestion code, dealing with real codec and streaming decisions (what to encode in, how to transport and play back efficiently over constrained store networks), figuring out what stays on-site versus what moves to the cloud, and getting the first features hooked up. This has a lot in common with building a video player/streaming stack. We need someone who's comfortable with some ambiguity: requirements that aren't fully defined yet, tooling you may need to build yourself, and a willingness to just build the thing rather than wait for a full spec. This is a hands-on coding role first, an infrastructure-design role second. We maintain a flat, high-impact engineering culture where every engineer who joins our team carries the title of Member of Technical Staff.

What you'll bring

• 5+ years relevant coding experience, with a track record of shipping production code yourself.
• B.S. or higher in Computer Science (or equivalent work experience)
• Strong Python, plus comfort picking up whatever language or tool the problem actually needs
• Hands-on experience with video codecs, encoding, and streaming protocols: video player, media pipeline, or streaming platform work (think the engineering behind a video player at a company like Crunchyroll, Netflix, Twitch, YouTube)
• Experience touching both sides of a pipeline, edge/device code and cloud services, rather than only one layer
• Has built something from zero before: a new service, a new team's first system, a 0-to-1 project with no existing scaffolding
• Comfortable being one of the few people who know how a piece of the system works, at least for a while
• Experience with camera or video hardware in the field (not just cloud-side video), especially dealing with inconsistent hardware or network conditions
• Comfortable working alongside ML engineers without needing to own the modeling work yourself
• Knowledge of infrastructure as code (Terraform, Ansible, etc.) and cloud environments like AWS
• Good knowledge of algorithms and data structures
• Fluent with AI coding tools (Claude Code, Cursor, etc.) as a default part of how you build and prototype

Benefits