Principal Software Engineer, ML Platform

About DAT

DAT Freight & Analytics is an award-winning employer of choice and a next-generation SaaS technology company that has been at the leading edge of freight and logistics innovation for nearly five decades. Founded in 1978, DAT operates the largest freight marketplace in North America — processing 250 million+ load posts annually and maintaining one of the largest repositories of freight market transaction data in the world.

On a defined path to $1 billion in revenue, DAT deploys a suite of software solutions, machine learning models, and intelligent automation tools that help brokers, carriers, and shippers price freight accurately, source capacity, reduce risk, and operate more efficiently. With nearly 700 teammates across offices in Denver, CO;
Portland, OR;
Seattle, WA;
Springfield, MO;
Toronto, ON; and Bangalore, India, DAT combines the credibility of a multi-decade market leader with the drive of a company that is not done disrupting the industry it helped build. For more information, visit

09/30/2026

DAT’s Science organization is seeking a Principal Software Engineer, ML Platform to lead the evolution of DAT’s most critical ML Platform capabilities.

As the platform enters a new phase of growth, we must increase our ability to experiment, learn, and adapt in real time across our marketplace, fraud detection, pricing, and other decision systems. We also need to scale and adapt ML capabilities built for sub-brands such as Convoy so they operate reliably within DAT’s broader product, data, and operational environment.

This role is both deeply hands‑on and highly architectural. The Principal Engineer will lead a 3‑4 member platform engineering team and set the technical direction for the foundational infrastructure that enables our ML and AI systems to iterate faster, adapt in real time, and operate safely at scale.

You will lead the development of the core capabilities that let us:

• Deliver lower‑latency data to models, unlocking online learning, adaptive policies, and improved real‑time decision‑making for our auction mechanisms, fraud detection systems, pricing workflows, and carrier engagement campaigns.
• Evolve our ML platform to support generative AI, including orchestration, retrieval, standardized service patterns, and scalable model serving needed for foundational model applications in document digitization and voice‑based features.
• Experiment faster and safer through robust causal‑inference tooling, richer randomized experimentation, and reliable evaluation infrastructure that helps us learn more about the unique spatio‑temporal dynamics of a trucking marketplace.
• Scale and operationalize ML models and platform capabilities for DAT’s scale, ensuring that differences in data, traffic, latency, reliability, and product integration are addressed systematically.

You will define and implement durable service architectures, build the real‑time systems that power ML in production, lead the platform engineering team, and partner closely with scientists and product engineers to accelerate iteration and innovation.

Your work will form the backbone of the next generation of ML and AI capabilities across the unified freight network DAT represents.

As a Principal Software Engineer, ML Platform, you will lead the technical direction, execution, and evolution of the ML platform across DAT. You will manage the technical priorities of a 3‑4 member platform engineering team, mentor engineers and scientists, and deliver solutions whose impact scales across teams and the broader organization, not just within individual projects.

Your work will influence three major areas:

Drive the evolution of DAT’s experimentation and model‑evaluation foundations. Enable rigorous causal measurement, reliable online experimentation, scalable model iteration, and adaptive learning systems that continuously improve marketplace and policy decisions.

• Evolve our various experimentation platforms to support richer randomized experiments, robust causal‑inference tooling, and high‑quality exposure and assignment…