Principal Software Engineer, ML Platform

DAT•Seattle, WA

7h•Hybrid

About The Position

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.

Requirements

Extensive experience in ML engineering, data infrastructure, platform engineering, or closely related production engineering roles, with demonstrated impact at Principal or equivalent scope.
Deep hands-on experience with real-time ML platforms, including feature stores, stream processing, low-latency data services, and online inference systems.
Strong proficiency in Python, with the ability to work across non-Python stacks including TypeScript/Node, gRPC services, and Kubernetes-based microservice ecosystems.
Expertise in modern data and ML infrastructure, including Kafka, Kubernetes, Postgres-like OLTP systems, cloud platforms, and production observability tooling.
Experience building and operating robust data and ML pipelines, both batch and streaming, in high-scale environments such as marketplaces, fraud detection systems, pricing, personalization, or real-time decision platforms.
Strong DevOps and MLOps fundamentals, including CI/CD, containerization, infrastructure-as-code such as Terraform and Helm, automated monitoring, and cloud cost and performance optimization.
Track record of leading and mentoring a small engineering team while remaining hands-on in architecture and implementation.
Experience scaling and adapting production systems across businesses, platforms, or materially different operating environments.
Collaborative platform mindset, with a track record of partnering with scientists and product engineers to co-design durable service patterns for model serving, deployment, monitoring, and API design.
Ability to set technical direction across teams, identify and retire platform risk, and deliver solutions whose impact scales across the organization.

Nice To Haves

Experience building ML systems in two-sided marketplaces, financial markets, or other economically complex environments, with intuition for incentives, pricing, and market dynamics.
Deep experience with data reliability and correctness at scale, including schema evolution, data-quality enforcement, backfills, late-data handling, and incident response for production data systems.
Applied advanced ML techniques such as reinforcement learning, bandits, or optimization to unlock real-world business impact, ideally within freight, logistics, or transportation technology.
Led the integration or migration of ML platforms across brands, business units, or significantly different production environments.
Experience building strong developer experience and platform adoption across multiple science and engineering teams.

Responsibilities

Lead the technical direction, execution, and evolution of the ML platform across DAT.
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.
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 logging; evaluate and integrate third-party solutions where beneficial.
Enable adaptive learning workflows, including reinforcement learning, contextual bandits, and online learning, for dynamic marketplace and policy decisions so scientists can safely run explore-exploit strategies in production.
Harden evaluation infrastructure across offline and online paths, including drift detection, guardrail metrics, and structured feedback loops that ensure reliable model behavior over time.
Develop orchestration patterns and tooling that combine inference, retrieval, business logic, policy guardrails, and human-in-the-loop steps into reusable, observable, and auditable workflows for ML-powered systems.
Establish cross-team standards for experimentation, evaluation, model feedback, and production readiness.
Lead the evolution of the low-latency feature store and real-time streaming platform that supports marketplace optimization, fraud detection, pricing, and other decision systems.
Iterate on and expand on existing low-latency feature store and real-time streaming platform (RisingWave) to deliver signals such as app analytics, carrier behavior, and digital fingerprints.
Ensure unified online and offline semantics to improve online decision-making, support real-time optimization, and enable future reinforcement-learning and online-learning workflows.
Build high-throughput streaming pipelines for carrier engagement, risk indicators, and fraud signals that power sub-minute marketplace and policy decisions.
Develop platform-level trucking knowledge systems, including RAG indexes, domain adapters, structured benchmarks, and retrieval strategies that ground AI systems in operational realities.
Lead the scaling and adaptation of ML capabilities for DAT’s traffic, data, reliability, and integration requirements.
Own the evolution of DAT’s ML Platform end to end, from data capture and transmission to storage and consumption by ML models and analytics.
Reduce latency, increase reliability, and improve developer efficiency across teams.
Set the technical roadmap for the platform ecosystem, leveraging Kafka, Snowflake, Kubernetes, and modern data formats such as Avro, JSON, and Iceberg.
Use Python and Go to build the connective tissue that ensures platform reliability and scale.
Build low-latency, production-grade services in Python and contribute to TypeScript/Node where needed, including emitting high-quality data signals, wiring model calls into product flows, and enabling experimentation and feature-flag pathways.
Partner with scientists and product engineers to define durable service patterns for API design, serving workflows, deployment, monitoring, and production ownership.
Mature core platform infrastructure, including Terraform/IaC, CI/CD, observability, logging and tracing, incident readiness, and cost/performance optimization.
Improve SQL/dbt workflows and batch and streaming pipelines to increase reliability, correctness, and scalability.
Extend model-serving infrastructure to support advanced ML workloads, from managed inference to self-hosted GPU, with standardized versioning, canary and A/B rollouts, and granular monitoring.
Establish engineering practices for safely scaling models and services from sub-brand environments into DAT production environments.
Lead and mentor a 3-4 member platform engineering team, setting priorities and raising the quality bar for platform design, implementation, and operations.
Define and communicate the multi-year technical direction for the ML platform in partnership with Science, Product, and Engineering leadership.
Make architecture decisions that affect multiple teams and product surfaces across DAT.
Identify and retire platform, reliability, data-quality, and operational risks before they constrain product delivery.
Build alignment across teams without relying solely on direct reporting authority.
Balance near-term product commitments with durable platform investments and reusable standards.