Senior Software, Data Platform (Remote OK)

Andrenam closed a $10M seed in 36 hours, led by First Round Capital with Also Capital, Long Journey, Homebrew, Banter, 201, Wavefunction, and the Colorado School of Mines Venture Fund. CEO Matej Cernosek is ex-SpaceX; CTO Alex Chu came out of Qualia. The ~15-person team is stacked with engineers from SpaceX, Anduril, Saronic, Palantir, ABL Space, and Arc. This is not a napkin-and-a-deck story, Andrenam is already deploying next-generation buoys off the California coast and was selected for the Navy's ANTX Coastal Trident, where it meshed multiple units and fused live sonar in the cloud.

Andrenam is building the sonar mesh for the ocean: a distributed network of low-cost, semi-attritable smart buoys that listen above and below the surface and stream acoustic data to the cloud, where ML localizes, classifies, and tracks vessels in real time. Each buoy carries solar, a battery pack, GPS, AIS, environmental sensors, and a Starlink backhaul, with an array of hydrophones below; fused across a field of nodes, time-difference-of-arrival becomes dots on a map, submarines for the Navy, unmanned underwater vehicles around ports and critical infrastructure, and surface traffic for the Coast Guard.

It replaces Cold War-era SOSUS "sonar shacks" with persistent, autonomous awareness of the last dark domain. The team works out of Torrance, CA.

Andrenam is hiring the first dedicated engineer for its data platform. Today the platform is nascent, solid backend infrastructure exists, but nobody owns it, and the perception/ML team is being handed data that isn't in the shape they need. This is a founding-level, zero-to-one seat: you'll define how raw maritime signals become clean, consistent, labeled datasets for perception and foundation models. You'll architect high-throughput pipelines that ingest real-time acoustic and telemetry data, then align, resample, calibrate, and restructure it, not necessarily in the moment (it can be matched up every 30 minutes, hour, or day) but into exactly what the ML team needs.

Expect to build backfill/reprocessing frameworks, lineage and versioning for reproducibility, dataset discovery APIs, data-quality instrumentation, and, likely, a labeling tool from scratch. There's real backend infra to build on, but the shape of the platform is yours to define.