The RoleWe'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 & EquityEarly-stage compensation: 130 ~ 150k salary, ~1.5%
experience and location - we're happy to talk through the details early in the process.
