Job description

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 www.DAT.com Job Application Deadline: 11/30/2026 The Opportunity As a Sr AI Engineer, you're an integral part of the team who will build and own the platform that power our agentic solutions at DAT. Freight is an uncertain business, and the models we ship reduce that uncertainty: rate forecasts, load-to-truck matching, document extraction, fraud signals, and the agentic workflows our brokers and carriers use to move freight faster. None of that reaches a customer without a platform that makes training, serving, evaluating, and monitoring models routine instead of heroic. You'll help build the platform, from the model gateway and inference layer through feature and vector storage, evaluation harnesses, production observability, agentic framework (A2A) all on a spine based on secure access and utilization. This is a highly visible role for an engineer who wants their work multiplied across every AI team in the company. What You'll Do Build and Ship: Write production code for the agentic services and platform components that power DAT's agentic mesh. This powers the systems our product and engineering counterparts build from. Deliver Against Specification: Build an approved specification which includes the problem, the non-goals, the non-functional budget, the trust boundary it crosses and deliver working software against it. Flag drift between what was asked for and what you were given before you write the first line, not after. Own Your Tests: Write the test suite for what you build including unit, integration, and evals from the specification itself, not from your own implementation, so a green suite means the requirement is met, not that the code agrees with itself. Your tests and observability are what raise, or cap, the repository's autonomy ceiling. Build Agentic Systems: Build and operate agents, tools, and multi-agent workflows on DAT's agent mesh which includes tool-calling, retrieval, memory, and orchestration that run inside the entitlements resolved for the request and never reach past them. AI Infrastructure on AWS: Build and operate production AI infrastructure on AWS — Bedrock,Agent Runtime Core, Strands, Lambda, S3, etc Design: You don't own the architecture, but you do own designs built from the premise. Bring informed pushback to decisions made by the team, flag what won't hold up in production, and show up to the conversation with an opinion, not just an implementation. Author Designs: Write the high-level design (HLD) for what you build. The design, the tradeoffs you made, and why so the next engineer, and the next team, can pick it up without you in the room. Review for Fidelity: Review AI-generated and human-written changes for the systems you own, holding the line on fidelity to the spec rather than on formatting, which is already settled by code. Respond to Incidents: Carry on-call for the services you build, investigate and resolve incidents in your own systems, and turn root cause into a fix rather than a postmortem slide. Publish What You Build: Put finished skills, agents, and components into DAT's capability marketplace so the next team that needs it finds it instead of rebuilding it. The Skills and Experience You'll Bring 5+ years of experience in software engineering, with real production ownership and solutions you shipped, operated, and got paged for. Demonstrated experience building with LLMs and agentic frameworks in production: tool-calling, retrieval-augmented generation, multi-agent orchestration (e.g., LangGraph, Bedrock Agents, MCP-style tool integration) which shipped and operated, not a weekend prototype. Strong development skills in TypeScript/ js , Python, Java, or Go, with the discipline to write tests other engineers actually trust. Solid, hands-on AWS experience: Bedrock,Agent Core Runtime, Strands, EKS/Kubernetes, Lambda, and S3 in production; working familiarity with Redpanda, MSK, or Kafka; comfortable in infrastructure-as-code (Pulumi, Terraform, or similar) even where you're not the one designing the account structure. Practical experience building or maintaining evaluation and monitoring for AI systems: eval sets, drift and quality signals, and treating a model regression with the same seriousness as a code regression. Comfortable working from a written specification rather than a verbal task. Ability to spot what's missing or ambiguous in a spec and push back before code, not after. Experience authoring design docs, HLDs, or short technical papers that another engineer can execute against without you there to explain it. Solid grounding in event-driven and distributed systems. You understand the tradeoffs Redpanda, MSK, kafka and relevant patterns force even if you're not the one drawing the topology. Working knowledge of observability practices — dashboards, traces, logs, and alerts sufficient to diagnose your own service and argue for what it needs to hit our authored autonomy tiers. Clear written and verbal communication — you'll be explaining tradeoffs to Staff and Principal engineers, to Product, and to non-technical stakeholders, not just to other engineers. Bonus Skills Familiarity with A2A, MCP (Model Context Protocol), or comparable agent-interop patterns. Experience publishing to, or consuming from, an internal skill or agent registry / capability marketplace. Experience with model fine-tuning, distillation, or parameter-efficient training, and a clear point of view on when it beats prompting. Freight, logistics, or supply chain domain experience. 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 *This position is not eligible for visa sponsorship** For Washington-based candidates, in compliance with the Washington State Pay Transparency Law, the salary range for this role is $162,000.00 - $207,000.00 + target bonus. 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) #LI-RF1 #LI-hybrid

Requirements

Must-haves

Build and Ship: Write production code for the agentic services and platform components that power DAT's agentic mesh. This powers the systems our product and engineering counterparts build from.
AI Infrastructure on AWS: Build and operate production AI infrastructure on AWS — Bedrock,Agent Runtime Core, Strands, Lambda, S3, etc
Author Designs: Write the high-level design (HLD) for what you build. The design, the tradeoffs you made, and why so the next engineer, and the next team, can pick it up without you in the room.
Review for Fidelity: Review AI-generated and human-written changes for the systems you own, holding the line on fidelity to the spec rather than on formatting, which is already settled by code.
Respond to Incidents: Carry on-call for the services you build, investigate and resolve incidents in your own systems, and turn root cause into a fix rather than a postmortem slide.
Publish What You Build: Put finished skills, agents, and components into DAT's capability marketplace so the next team that needs it finds it instead of rebuilding it.
The Skills and Experience You'll Bring
5+ years of experience in software engineering, with real production ownership and solutions you shipped, operated, and got paged for.
Strong development skills in TypeScript/ js , Python, Java, or Go, with the discipline to write tests other engineers actually trust.
Practical experience building or maintaining evaluation and monitoring for AI systems: eval sets, drift and quality signals, and treating a model regression with the same seriousness as a code regression.
Comfortable working from a written specification rather than a verbal task. Ability to spot what's missing or ambiguous in a spec and push back before code, not after.
Experience authoring design docs, HLDs, or short technical papers that another engineer can execute against without you there to explain it.
Solid grounding in event-driven and distributed systems. You understand the tradeoffs Redpanda, MSK, kafka and relevant patterns force even if you're not the one drawing the topology.
Working knowledge of observability practices — dashboards, traces, logs, and alerts sufficient to diagnose your own service and argue for what it needs to hit our authored autonomy tiers.
Clear written and verbal communication — you'll be explaining tradeoffs to Staff and Principal engineers, to Product, and to non-technical stakeholders, not just to other engineers.
Familiarity with A2A, MCP (Model Context Protocol), or comparable agent-interop patterns.
Experience publishing to, or consuming from, an internal skill or agent registry / capability marketplace.
Experience with model fine-tuning, distillation, or parameter-efficient training, and a clear point of view on when it beats prompting.
Freight, logistics, or supply chain domain experience.
Why DAT? DAT is an award winning employer of choice.
Medical, Dental, Vision, Life, and AD&D insurance
Flexible Vacation Time (FVT)
Employee Stock Purchase Plan

Nice-to-haves

Bonus Skills

Application prep

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Keywords to mirror

machine learningaiawstypescriptpythonjavagokubernetes

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Paid Time Off

Parental Leave

Wellness