Job description

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 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. 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). 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. 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. 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. 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 Experience: 5+ years of professional experience building, deploying, and maintaining machine learning models in production environments. 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). High-Velocity Data Engineering: Proven ability to work with large-scale, high-throughput streaming data (IoT, GPS pings, sensor data) and distributed processing frameworks. Software Engineering: Production-level Python proficiency, strong API design habits (e.g., FastAPI, Pydantic), object-oriented programming, and robust automated testing practices. MLOps Foundations: Hands-on experience with model deployment, containerization (Docker), monitoring tools, and cloud infrastructure (AWS). Languages: Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow), SQL. Data Infrastructure: PostgreSQL (PostGIS for geospatial), Apache Kafka / Kinesis (for streaming telematics), Redis, Snowflake, AWS CDK (Python). MLOps & Cloud: Docker, AWS (SageMaker, ECS, S3), GitHub Actions. Preferred 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 Time-series or geospatial data at scale: geofencing, map matching, trip and stop inference, H3 or similar indexing Data-sharing and multi-tenant delivery patterns: Snowflake shares, per-tenant credential isolation, consent-scoped access Experience building against many third-party APIs at once , where you control neither the schema nor the uptime Working in a SOC 2 environment , or helping get a company there Why Catena, Why Now Early-stage startup: high ownership, real impact, and a seat at the table Work directly with leadership and have your voice heard on product and API direction Be the technical authority customers rely on, and help build the Sales Engineering function from the ground up Remote-friendly environment Catena has raised $8.25M from Shaper Capital, Floating Point, Plug and Play, Liquid 2, and leading industry angels. Customers are already using us in production including modern TMS platforms (Mastery, Optym), large brokerages, and mid-to-large direct carriers operating 100–1,000+ trucks. Many of them are also investors. Compensation: Competitive salary, full benefits, equity and 401k match. US only, no sponsorship available. Occasional travel for team on-sites.

Requirements

Must-haves

We're hiring an AI/ML Engineer to advance the intelligence side of the Catena platform.
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.
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.
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.
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.
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
Experience: 5+ years of professional experience building, deploying, and maintaining machine learning models in production environments.
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).
High-Velocity Data Engineering: Proven ability to work with large-scale, high-throughput streaming data (IoT, GPS pings, sensor data) and distributed processing frameworks.
Software Engineering: Production-level Python proficiency, strong API design habits (e.g., FastAPI, Pydantic), object-oriented programming, and robust automated testing practices.
MLOps Foundations: Hands-on experience with model deployment, containerization (Docker), monitoring tools, and cloud infrastructure (AWS).
Languages: Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow), SQL.

Nice-to-haves

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
Time-series or geospatial data at scale: geofencing, map matching, trip and stop inference, H3 or similar indexing
Data-sharing and multi-tenant delivery patterns: Snowflake shares, per-tenant credential isolation, consent-scoped access
Experience building against many third-party APIs at once , where you control neither the schema nor the uptime
Working in a SOC 2 environment , or helping get a company there
Why Catena, Why Now
Early-stage startup: high ownership, real impact, and a seat at the table
Work directly with leadership and have your voice heard on product and API direction
Be the technical authority customers rely on, and help build the Sales Engineering function from the ground up
Remote-friendly environment
US only, no sponsorship available. Occasional travel for team on-sites.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

apiaimlmachine learningpythondockerawssql

Platform: lever
Effort: quick

Benefits

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401k