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DAT Freight & Analytics

On-demand freight marketplace with analytics

Senior AI Engineer - Emerging AI

AI Application Engineering +1Updated on Nov 9
Full-timeSeattle, WA, USA

About the Job

Requirements

  • At least 5 years of software engineering experience with production ownership, including solutions shipped, operated, and supported on call.
  • Demonstrated production experience building with large language models and agentic frameworks, including tool-calling, retrieval-augmented generation, and multi-agent orchestration.
  • Strong development skills in TypeScript, JavaScript/Node.js, Python, Java, or Go, and the ability to write reliable tests.
  • Hands-on production experience with AWS services including Bedrock, Agent Core Runtime, Strands, EKS/Kubernetes, Lambda, and S3; working familiarity with Redpanda, MSK, or Kafka; and comfort with infrastructure-as-code such as Pulumi or Terraform.
  • Practical experience building or maintaining AI evaluation and monitoring, including evaluation sets, drift and quality signals, and model-regression monitoring.
  • Ability to work from written specifications, identify missing or ambiguous requirements, and raise issues before implementation.
  • Experience authoring design documents, high-level designs, or technical papers that another engineer can execute independently.
  • Grounding in event-driven and distributed systems and understanding of relevant tradeoffs and patterns in Redpanda, MSK, and Kafka.
  • Working knowledge of observability practices, including dashboards, traces, logs, and alerts, sufficient to diagnose services and assess requirements for autonomy tiers.
  • Clear written and verbal communication to explain tradeoffs to engineering, product, and non-technical stakeholders.

Responsibilities

  • Write production code for the agentic services and platform components powering DAT's agentic mesh.
  • Build approved specifications covering the problem, non-goals, non-functional budget, and trust boundary, and deliver working software against them; identify specification drift before implementation.
  • Write unit, integration, and evaluation tests derived from the specification.
  • Build and operate agents, tools, and multi-agent workflows on DAT's agent mesh, including tool-calling, retrieval, memory, and orchestration within the request's resolved entitlements.
  • Build and operate production AI infrastructure on AWS, including Bedrock, Agent Runtime Core, Strands, Lambda, and S3.
  • Create designs from established premises, provide informed technical feedback, and flag production risks.
  • Write high-level designs for developed components, documenting decisions and tradeoffs for future engineers and teams.
  • Review AI-generated and human-written changes to ensure fidelity to specifications.
  • Participate in on-call support for owned services, investigate and resolve incidents, and turn root causes into fixes.
  • Publish completed skills, agents, and components to DAT's capability marketplace.

Desired Qualifications

  • Familiarity with A2A, Model Context Protocol, or comparable agent-interoperability patterns.
  • Experience publishing to or consuming from an internal skill or agent registry or capability marketplace.
  • Experience with model fine-tuning, distillation, or parameter-efficient training, and a reasoned perspective on when these approaches are preferable to prompting.
  • Freight, logistics, or supply chain domain experience.

About the Company

DAT Freight & Analytics

On-demand freight marketplace with analytics

DAT Freight & Analytics runs the DAT Network, North America’s largest on-demand freight marketplace, linking shippers who need to move goods with carriers who transport them and brokers who facilitate transactions. Its core offering is a subscription-based platform that includes a robust load board and analytics tools, with DAT iQ providing market intelligence to help plan and optimize freight operations. The company differentiates itself by the size of its network, its long history dating back to 1978, and its focus on data-driven optimization and automation. Its goal is to make freight matching more reliable and efficient by improving visibility, reducing uncertainty, and speeding up the transportation process for brokers, carriers, and shippers.

Company size
501-1,000
Company stage
Acquired
Total funding
Not listed
Headquarters
Beaverton, OR
Founded
1978

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