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Catena Clearing

Universal logistics data API integration

Senior AI/ML Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listed

About the Job

Requirements

  • At least 5 years of professional experience building, deploying, and maintaining machine-learning models in production environments.
  • Deep background in time-series forecasting, geospatial data analysis, anomaly detection, and classic machine-learning algorithms such as XGBoost, Random Forests, and clustering techniques.
  • Proven ability to work with large-scale, high-throughput streaming data such as IoT, GPS pings, and sensor data, and distributed processing frameworks.
  • Production-level Python proficiency, API design experience with tools such as FastAPI and Pydantic, object-oriented programming, and robust automated testing practices.
  • Hands-on experience with model deployment, containerization using Docker, monitoring tools, and cloud infrastructure such as AWS.
  • Experience with Python libraries including Pandas, NumPy, Scikit-learn, PyTorch or TensorFlow, and SQL.
  • Experience with PostgreSQL and PostGIS for geospatial data, Apache Kafka or Kinesis for streaming telematics, Redis, Snowflake, and AWS CDK using Python.
  • Experience with Docker and AWS services including SageMaker, ECS, and S3, as well as GitHub Actions.

Responsibilities

  • Build and scale machine-learning pipelines to ingest, clean, and normalize messy, high-frequency time-series data across diverse ELD providers and vehicle sensor networks.
  • Use geospatial tracking and time-series forecasting to extract actionable insights from millions of daily data points, including dynamic ETAs, route optimization, and driver safety scoring.
  • Develop and deploy algorithms to identify anomalous behaviors, location spoofing, and irregular route deviations to protect brokers, factoring companies, and load boards from freight fraud.
  • Architect models for predictive maintenance, intelligent dispatching, and risk scoring by synthesizing historical records, Hours of Service data, and real-time telematics.
  • Optimize model latency and throughput so predictive features and data enrichments are delivered through Catena’s low-latency API without degrading system performance.
  • Partner with backend engineers and product teams to integrate ML microservices into Catena’s core infrastructure and bring new data products to market.

Desired Qualifications

  • Experience in telematics, IoT, logistics, fintech, or insurance data, particularly data with physical-world ground truth used for financial decisions.
  • Experience working with time-series or geospatial data at scale, including geofencing, map matching, trip and stop inference, and H3 or similar indexing.
  • Experience with data-sharing and multi-tenant delivery patterns, including Snowflake shares, per-tenant credential isolation, and consent-scoped access.
  • Experience building against many third-party APIs where the schemas and uptime are not controlled by the team.
  • Experience working in a SOC 2 environment or helping a company achieve SOC 2 compliance.

About the Company

Catena Clearing

Universal logistics data API integration

Catena Clearing provides a universal data API that standardizes logistics data from many sources, acting as neutral data plumbing inside customers' stacks. Its Catena Telematics API connects with over 75 ELDs and telematics providers, covering about 95% of the market to deliver normalized real-time and historical data for load matching, fraud prevention, risk scoring, and compliance. By unifying data, it eliminates the need for hundreds of individual integrations, helping partners in freight brokerage, insurance, and fintech scale their applications. Its goal is to reduce data fragmentation in trucking, expand across North America, and eventually extend into global multimodal logistics including ports, rail, and air.

Company size
11-50
Company stage
Seed
Total funding
$5M
Headquarters
New York City, New York
Founded
2023

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