Senior AI/ML Engineer

About Catena

Catena is the universal API for fleet telematics data. We give insurers, factors, TMS providers, brokers, fintechs, and freight-tech platforms a single integration to access carrier vehicle data across every major ELD and telematics provider, replacing dozens of one-off connections. Carriers opt in through a quick, permissioned flow, and our customers immediately get standardized, real-time data on a clean, normalized layer.

About the Role

We're hiring an AI/ML Engineer to advance the intelligence side of the Catena platform.

Responsibilities

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Telematics Data Normalization: Build and scale ML pipelines to ingest, clean, and normalize messy, high-frequency time-series data across diverse ELD providers and vehicle sensor networks.

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Geospatial & Time-Series Modeling: Leverage geospatial tracking and time-series forecasting to extract actionable insights from millions of daily data points (e.g., dynamic ETAs, route optimization, driver safety scoring).

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Anomaly & Fraud Detection: Develop and deploy algorithms to identify anomalous behaviors, location spoofing, or irregular route deviations to protect brokers, factoring companies, and load boards from freight fraud.

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Predictive Analytics: Architect models for predictive maintenance, intelligent dispatching, and risk scoring by synthesizing historical records, Hours of Service (HOS) data, and real-time telematics.

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Real-Time Inference & MLOps: Optimize model latency and throughput to ensure predictive features and data enrichments are delivered seamlessly through Catena’s low-latency API without degrading system performance.

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Cross-Functional Collaboration: Partner closely with backend engineers and product teams to integrate ML microservices into Catena’s core infrastructure and bring new data products to market.

Qualifications & Tech Stack

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Experience: 5+ years of professional experience building, deploying, and maintaining machine learning models in production environments.

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Core ML Expertise: Deep background in time-series forecasting, geospatial data analysis, anomaly detection, and classic machine learning algorithms (e.g., XGBoost, Random Forests, clustering techniques).

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High-Velocity Data Engineering: Proven ability to work with large-scale, high-throughput streaming data (IoT, GPS pings, sensor data) and distributed processing frameworks.

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Software Engineering: Production-level Python proficiency, strong API design habits (e.g., FastAPI, Pydantic), object-oriented programming, and robust automated testing practices.

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MLOps Foundations: Hands-on experience with model deployment, containerization (Docker), monitoring tools, and cloud infrastructure (AWS).

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Languages: Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow), SQL.

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Data Infrastructure: PostgreSQL (PostGIS for geospatial), Apache Kafka / Kinesis (for streaming telematics), Redis, Snowflake, AWS CDK (Python).

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MLOps & Cloud: Docker, AWS (SageMaker, ECS, S3), GitHub Actions.

Preferred

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Experience in telematics, IoT, logistics, fintech, or insurance data — anywhere the data has physical-world ground truth and someone makes a money decision on it

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Time-series or geospatial data at scale: geofencing, map matching, trip and stop inference, H3 or similar indexing

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Data-sharing and multi-tenant delivery patterns: Snowflake shares, per-tenant credential isolation, consent-scoped access

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Experience building against many third-party APIs at once , where you control neither the schema nor the uptime

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Working in a SOC 2 environment , or helping get a company there