Senior Software Engineer, Data Platform

About the Company

A defense tech company that closed an oversubscribed seed round in a matter of days, backed by a well-known early-stage fund alongside several other institutional investors. The founding team has deep backgrounds in aerospace and hardware engineering, and the roughly 15-person team includes engineers from some of the most respected names in defense and aerospace technology. This isn't an early concept, the company is already deploying next-generation smart buoys off the California coast and has been selected to participate in a real US Navy technology demonstration, successfully meshing multiple units and fusing live sonar data in the cloud.

The company is building a distributed network of low-cost sensor buoys that listen above and below the ocean's surface and stream data to the cloud, where machine learning localizes, classifies, and tracks vessels in real time. Each buoy carries solar power, a battery pack, GPS, environmental sensors, and satellite backhaul, with an array of underwater listening devices. Fused across a field of these nodes, the system produces persistent, autonomous awareness for the Navy, for unmanned vessel detection around ports, and for surface traffic monitoring for the Coast Guard.

About the Role

This is the first dedicated engineer for the company's data platform. Today, solid backend infrastructure exists, but nobody owns it end to end, and the machine learning team is working with 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.

What You'll Own

• High-throughput pipelines that ingest real-time acoustic and telemetry data, then align, resample, calibrate, and restructure it into exactly what the machine learning team needs
• Backfill and reprocessing frameworks for new filters, syncs, label corrections, and metadata enrichment across historical data
• Lineage and versioning to guarantee experiment reproducibility
• Dataset discovery and access APIs, queryable by time, region, modality, labels, and quality flags
• Data-quality metrics, dashboards, and alerts
• Likely: building a labeling tool and pre-labeling workflows from scratch for the perception team

What We're Looking For

• Strong data-pipeline architecture experience, with the ability to design a system from a blank page, unassisted
• Solid depth with at least one cloud provider, preferably AWS, and real experience deploying pipelines to production
• Python plus data tooling (PyArrow, Polars, Pandas, NumPy, SciPy), along with one services language such as Go, Rust, or TypeScript
• Roughly 5 to 8 years of experience, with startup or greenfield time weighted heavily, 7 or more years if that experience comes from slower-moving companies
• A low-ego, highly autonomous builder who can come in and start producing without significant hand-holding
• Bonus: exposure to edge or sensor data (audio, sonar, video, telemetry), time synchronization, or geospatial context; experience designing labeling workflows or working with orchestration and lineage tooling

Logistics

Remote is fine, though candidates based in the Los Angeles area are strongly preferred, with occasional on-site visits to the company's Southern California office. US citizenship is required for this role. Compensation is $200K to $240K plus 0.25% to 0.5% equity.