Machine Learning Engineer

Company Description and The role

Philyron (a16z SR) Working with the co-founders, you will own the entire pipeline from vessel-edge connectivity (MQTT/Sparkplug B) to cloud architecture (AWS) and the ML fusion engine that predicts failure modes using sensor and historical data. Full Stack preferred.

Concretely, that means everything after the hardware: getting data off the vessel, into the cloud, onto a dashboard, and through the machine learning models that make the whole thing worth paying for. You won't be wiring sensors — but you'll own everything those sensors feed into.

What you'll build

Connectivity — get data off the ship. Ships have terrible, intermittent connectivity. You'll build the pipeline that moves sensor data from a vessel's edge device to our cloud reliably over satellite links (VSAT / Starlink) — using lightweight, fault-tolerant protocols (MQTT / Sparkplug B), with encryption in transit and graceful handling of connections that drop for days at a time and resume.

Cloud backbone — land it and make it usable. You'll stand up our cloud architecture (AWS): a real-time "hot path" that checks incoming sensor metrics against safety thresholds and fires instant alerts, and a "cold path" that stores high-frequency time-series data for trend analysis and model training. You'll also build the integration layer that pulls decades of historical maintenance records out of operators' existing CMMS databases — the data that makes our predictions possible.

Predictive fusion engine — the reason we win. This is the heart of it. You'll build the ML system that fuses two data streams almost no competitor combines: live sensor signatures and historical failure records. You'll map past maintenance events to the sensor patterns that preceded them, train anomaly-detection and failure-prediction models (e.g. isolation forests / autoencoders for anomalies, gradient-boosted trees / LSTMs for remaining-useful-life), and design the fleet-wide-plus-per-vessel modeling approach that makes predictions both accurate and personalized to each engine. The output isn't "anomaly detected" — it's "this pattern preceded a gearbox bearing failure across the fleet; inspect within 14 days."

Automated reporting. You'll build the service that compiles trends, history, and predictions into clean reports delivered to operators on a schedule — turning the platform into something that shows up in their inbox and proves its value every week.