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# Founding ML Engineer

- **Company:** A16Z SPEEDRUN, L.L.C.
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Cloud Engineering, Computerized Maintenance Management Systems, Databases, Fault Tolerance, Message Queuing Telemetry Transport (MQTT), Data Streaming, Boosted Trees, Recurrent Neural Networks
- **Published:** November 12, 2026
- **Apply:** https://www.careerbuilder.com/job-details/founding-ml-engineer-san-francisco-ca--fbec569d-f834-47fe-8ad5-4f1057d32103

## About the Role

Record Keeping, Vehicle Fleets, Cargo/Freight, Artificial Intelligence (AI), Cryptography, Cloud Computing, Sales Pipeline, Computerized Maintenance Management System (CMMS), Cloud Architecture, Metrics

## Description

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., * 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.