Staff Software Engineer, ML Platform

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. For additional information, see www.DAT.com/company

Job Application Deadline: 04/30/2026

The Opportunity

DAT’s Science org is seeking a Staff Software Engineer, ML Platform to scale and evolve DAT and 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 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 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.

What You'll Do

As a Staff Software Engineer, ML Platform, 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:

Experimentation and Evaluation Infrastructure

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 our experimentation platform (TestDrive) to support richer randomized experiments, robust causal inference tooling, and high-quality exposure/assignment logging; evaluate and integrate third-party solutions where beneficial.
• Enable adaptive learning workflows (reinforcement learning, contextual bandits, 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.

Feature Stores and Streaming Infrastructure

• Iterate on and expand Convoy Platform’s low-latency feature store and real-time streaming platform (RisingWave) to deliver signals such as app analytics, carrier behavior, and digital fingerprints for marketplace optimization, fraud detection, and other decision systems.
• Ensure unified online/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.

Core DevOps/MLOps Foundation

This role owns the evolution of Convoy’s Data and ML Platform end-to-end, from data capture and transmission to storage and consumption by ML models and analytics, reducing latency, increasing reliability, and improving developer efficiency.

• Design and scale the platform ecosystem, leveraging Kafka, Snowflake, Kubernetes, and modern data formats (Avro, JSON, Iceberg), and use Python/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 (e.g., emitting high-quality data signals, wiring model calls into product flows, enabling experimentation and feature-flag pathways).
• Partner with scientists to define durable service patterns (API design, serving workflows, monitoring) that enable fast, safe iteration on ML-backed services.
• Mature core platform infrastructure, including Terraform/IaC, CI/CD, observability, logging/tracing, incident readiness, and cost/performance optimization.
• Improve SQL/dbt workflows and batch/streaming pipelines to increase reliability, correctness, and scalability.
• Extend model-serving infrastructure to support advanced ML workloads (managed inference → self-hosted GPU), with standardized versioning, canary/A/B rollouts, and granular monitoring.

The Skills and Experience You'll Bring

• 6+ years of experience in ML engineering, data infrastructure, platform engineering, or closely related production engineering roles.
• 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), ideally 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 (Terraform/Helm), automated monitoring, and cloud cost and performance optimization.
• 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, enabling fast and safe iteration on ML-backed systems.
• Ability to operate at Staff scope, leading technical direction on key initiatives, identifying and retiring platform risk, mentoring engineers, and delivering solutions whose impact scales across teams and the broader organization.

We'd Be Extra Excited If

• You have experience building ML systems in two-sided marketplaces, financial markets, or other economically complex environments, with intuition for incentives, pricing, and market dynamics.
• You have 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.
• You have applied advanced ML techniques such as reinforcement learning, bandits, or optimization to unlock real-world business impact, ideally within freight, logistics, or transportation technology.

Why DAT?
DAT is an award winning employer of choice.

For starters, we have a hybrid work environment, but we also know what makes a great workplace. We have a time-tested and resolute set of operating values predicated on integrity, mutual respect, open communication, and executing with excellence. These values inform our strategic vision as much as any one of our products does. We’ve been an employer of choice in the Portland metropolitan area for four decades, and within one year of opening our Denver office, DAT was #26 on Built In Colorado’s 100 Best Places to Work In Colorado.

• Medical, Dental, Vision, Life, and AD&D insurance
• Parental Leave
• Flexible Vacation Time (FVT)
• An additional 10 holidays of paid time off per calendar year
• 401k matching (immediately vested)
• Employee Stock Purchase Plan
• Short- and Long-term disability sick leave
• Flexible Spending Accounts
• Health Savings Accounts
• Employee Assistance Program
• Additional programs - Employee Referral, Internal Recognition, and Wellness
• Free TriMet transit pass (Beaverton Office)
• Competitive salary and benefits package
• Work on impactful projects in a cutting-edge environment
• Collaborative and supportive team culture
• Opportunity to make a real difference in the trucking industry
• Employee Resource Groups

For Washington-based candidates, in compliance with the Washington State Pay Transparency Law, the salary range for this role is $186,000.00 - $271,000.00 + target bonus + benefits.  DAT considers factors such as scope and responsibilities of the position, candidate's work experience, education and training, core skills, internal equity, and market and business elements when extending an offer.

DAT embraces the value of a diverse workforce, and believes it is a core strength of our company that we encourage those values in every DAT employee, at every level of our organization, regardless of tenure or rank. We provide equal employment opportunities (EEO) to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state, and local laws.

Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities

The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60-1.35(c)

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