Staff Data Engineer

About Netradyne:

Netradyne provides AI-powered technologies for fleet management and safer roads. An award-winning industry leader in fleet safety and video telematics solutions, Netradyne empowers thousands of commercial fleet customers across North America, Europe, and Asia to enhance their driver performance, reduce risk, and optimize operations.

Netradyne sets the standard among transportation technology companies for enhancing and sustaining road safety, with an industry-leading 25+ billion miles vision-analyzed for risk and an industry-first driver scoring system that reinforces safe behaviors. Founded in 2015, Netradyne is headquartered in San Diego with offices in San Francisco, Nashville, the UK and Bangalore. For more details visit: www.netradyne.com

Job Overview:

As a Staff Data Engineer – ML at Netradyne, an award-winning industry leader in fleet safety and video telematics solutions, you will take senior technical ownership of the machine learning and data platforms that underpin our AI-powered fleet analytics. This role is responsible for designing, building, and scaling production-grade ML pipelines, generative AI capabilities, and real-time data streaming systems across cloud and edge environments, delivering actionable insights that make fleet operations safer and more efficient. You will collaborate closely with machine learning engineers, data scientists, and product teams to integrate advanced AI technologies into Netradyne's platform, including on-device edge intelligence and natural-language-driven capabilities. Your work will directly shape the data infrastructure behind Netradyne's Physical AI vision, where AI continuously interprets multimodal vehicle data to understand drivers, vehicles, and road environments as an integrated system.

Key Responsibilities:

• You will be embedded within a team of machine learning engineers and data scientists, responsible for building and productising generative AI, deep learning, and data engineering solutions. You will:
• Design, develop and deploy production-ready, scalable solutions that utilise generative AI, traditional ML models, data science workflows, and ETL/ELT pipelines on AWS cloud infrastructure (and hybrid edge-cloud environments).
• Build and manage real-time and batch data streaming pipelines to handle high-volume, high-velocity data from fleet devices, leveraging technologies such as Apache Kafka and Amazon Kinesis for low-latency processing and near real-time insights.
• Collaborate with cross-functional teams (Product, Data Science, ML Engineering, Operations) to integrate AI-driven solutions into business operations and customer-facing products.
• Design and implement generative AI solutions using large language models (LLMs), including prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) patterns, to enable intelligent analytics, natural-language interfaces, and context-aware insights across fleet and safety data.
• Build and scale agentic AI systems by developing autonomous and semi-autonomous agents that orchestrate data retrieval, reasoning, and actions across ML pipelines and services, ensuring these agentic workflows are production-grade, observable, and aligned with safety, reliability, and governance requirements.
• Implement MLOps best practices including CI/CD pipelines for model training, automated testing, model versioning, continuous monitoring, and modern workflow orchestration (e.g. Apache Airflow, MLFlow) to achieve reliable, reproducible model deployments at scale.
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