Founding ML Engineer
First seen by Alion on Nov 9, 2026.
About the Company
Philyron is building the predictive brain for commercial fleets from tankers, cargo ships to defense vessels. We use real-time sensor data and historical maintenance records to predict and explain engine failures before they happen.
The Role
We're hiring a founding ML engineer to own the AI systems at the heart of the product. You'll work directly with the founders and own the full loop: data, training, evals, and shipping models into production.
What You'll Do
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.
What We're Looking For
Evidence you've shipped ML that real users touched - in production, research, or serious projects
Show us projects, repos, demos, side projects - real things you've designed and built end to end, ideally ones that touched both data/ML and the infrastructure around it. Your portfolio is your résumé.
Strong fundamentals across the stack, not just notebooks
Pragmatism: you pick the boring approach that works over the clever one that might
Comfort owning systems end to end with no ML team behind you
Compensation & Equity
Early-stage compensation: 130 ~ 150k salary, ~1.5%
experience and location - we're happy to talk through the details early in the process.