Principal Machine Learning Engineer

About DAT

DAT is an award‑winning employer of choice and a next‑generation SaaS technology company that has been at the leading edge of innovation in transportation supply chain logistics for 45 years. We continue to transform the industry year over year, by deploying a suite of software solutions to millions of customers every day – customers who depend on DAT for the most relevant data and most accurate insights to help them make smarter business decisions and run their companies more profitably.

We operate the largest marketplace of its kind in North America, with 400 million freights posted in 2022, and a database of $150 billion of annual global shipment market transaction data. Our headquarters are in Denver, CO, and Beaverton, OR, with additional offices in Seattle, WA;
Springfield, MO; and Bangalore, India.

Job Final date to receive applications: 01/30/2026

DAT's Convoy Platform Science team is seeking a Principal ML Platform Engineer to scale and evolve Convoy's most critical Data and ML Platform capabilities. As the platform enters a new phase of growth, we must dramatically increase our ability to experiment, learn, and adapt in real time across our marketplace, fraud detection, and pricing systems. This role is both deeply hands‑on and highly architectural, responsible for building the foundational infrastructure that enables our ML and AI systems to move faster, learn faster, 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 Convoy's auction mechanism, fraud detection apparatus, 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 to help us learn more about the unique spatio‑temporal dynamics of a Trucking marketplace.

You will define and implement durable service architectures, build the real‑time systems that power ML in production, and partner closely with scientists to accelerate iteration and innovation. This is a pivotal moment in the integration of Convoy's technology into the broader DAT ecosystem, and your work will form the backbone of the next generation of ML and AI capabilities across the freight network.

As a Principal ML Platform Engineer, you will set technical direction, mentor other engineers and scientists, and deliver solutions whose impact scales across teams and the broader Convoy Platform, not just within individual projects. Your work will influence three major areas:

Drive the evolution of Convoy'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 Convoy's experimentation stack (Test Drive):
  Add richer randomized experiments, causal inference tooling, exposure/assignment logging, and metric pipelines; evaluate third‑party solutions where beneficial.
• Enable adaptive learning approaches (RL, contextual bandits, online learning) for dynamic marketplace and policy decisions (e.g., inferring the best timing, cohort, or communication channel to maximize carrier engagement).
• Harden our evaluation infrastructure, including offline/online pipelines, drift detection mechanisms, and structured feedback loops that ensure reliable model behavior over time.
• Implement orchestration layers that combine inference, retrieval, business logic, guardrails, and human‑in‑loop flows into reliable, auditable multi‑step AI agents.

Iterate on and expand Convoy Platform's low‑latency Feature Store and real‑time streaming platform (on…