DAT builds agentic solutions for freight and logistics, and this role helps power the platform behind them. As a Sr AI Engineer (Emerging AI), you will design, operate, and continuously improve the AI infrastructure that supports model training, serving, evaluation, monitoring, and governance, with a strong focus on AWS scalability, reliability, and cost-aware production delivery.
Platform and shared services
- Design, build, and operate shared services that enable engineers and business users to ship models, including a model gateway for LLM access, inference endpoints for real-time and batch scoring, feature storage, vector search, and a common SDK.
- Architect and support scalable AI systems by writing design documents and driving alignment across Product and Engineering and Business users on how models get built and shipped at DAT.
AI infrastructure on AWS
- Architect and run scalable, reliable AI infrastructure on AWS using services including Bedrock, SageMaker, EKS, Redpanda, MSK (Kafka), Lambda, and S3.
- Provision and manage infrastructure using Pulumi.
Evaluation, monitoring, and reliability
- Build offline and online evaluation systems to determine whether a model or prompt change improves outcomes, including regression suites, LLM-as-judge pipelines, A/B and shadow testing, and drift detection.
- Own inference cost and latency as first-class metrics, right-sizing GPU and serverless capacity and tuning batching, caching, and quantization while providing teams visibility into workload costs.
- Implement guardrails, prompt and output logging, PII handling, access controls, and model and dataset lineage to support security and customer data commitments.
Engineering practices and incident response
- Drive adoption of modern software development practices across AI work, including automated testing, code reviews, CI/CD pipelines, and infrastructure-as-code.
- Mentor engineers and data scientists on production ML and distributed systems, raising the operational bar across teams building on the platform.
- Lead response and resolution for complex production incidents involving AI services, perform root cause analysis, and implement preventative measures.
Required experience
- 10+ years of experience in software engineering, including substantial time as a senior or staff engineer owning production infrastructure relied upon by other engineering teams.
- 4+ years building and operating machine learning or AI platforms, taking models from notebook to production traffic and keeping them healthy.
Key skills
- Strong development language skills ideally using TypeScript/js, Java, or Go, along with the discipline to create libraries other teams adopt.
- Extensive AWS experience with production systems, ideally including EKS/Kubernetes, Lambda, Redpanda, MSK/Kafka, S3, and Secrets Manager, plus infrastructure-as-code with Pulumi, Terraform, or similar.
- Hands-on experience serving models at scale, including containerized inference, GPU scheduling, autoscaling, batching, and tradeoffs between real-time, streaming, and batch scoring.
- Practical experience with LLM systems in production, including retrieval-augmented generation, vector databases, prompt and context management, tool-calling or agent frameworks, and managed model APIs such as Amazon Bedrock.
- Experience building evaluation and monitoring beyond services, including offline eval sets, online metrics, drift and data quality checks, and clear definitions of model regression.
- Background in event-driven and distributed systems using Redpanda, AWS MSK, or Kafka.
- Fluency with observability and on-call health, designing dashboards, APM traces, logs, and alerts, defining SLOs, and using them to reduce incident frequency and MTTR.
- Experience establishing engineering standards, including PR and testing guidelines, deployment validation checklists, model release criteria, and post-incident review practices.
- Demonstrated technical leadership through cross-team designs, clear system design documents and operations runbooks, and alignment across Engineering, Product, and Operations.
- Proven mentoring track record focused on system design, observability, and operational excellence.
- Excellent communication skills to explain trade-offs and system behavior to technical and non-technical stakeholders.
Technologies
- TypeScript/js, Java, Go
- AWS (Bedrock, SageMaker, EKS, Redpanda, MSK, Lambda, S3)
- Pulumi, Terraform, EKS/Kubernetes
- Secrets Manager
- Vector search, retrieval-augmented generation, vector databases
- Tool-calling, agent frameworks
- LLM-as-judge pipelines, A/B testing, shadow testing
- Observability (APM traces)
Benefits
- Medical, Dental, Vision, Life, and AD&D insurance
- Parental Leave
- Flexible Vacation Time (FVT)
- 10 additional 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 and Health Savings Accounts
- Employee Assistance Program
- Employee Referral, Internal Recognition, and Wellness programs
- Free TriMet transit pass (Beaverton Office)
- Competitive salary and benefits package
- Collaborative and supportive team culture
- Opportunity to make a real difference in the trucking industry
- Employee Resource Groups
Location: Seattle, WA (hybrid)
Compensation: USD 162,000 - 207,000 per yearly
Experience: 10+ years
Application deadline: 11/30/2026
Bonus skills
- Experience with model fine-tuning, distillation, or parameter-efficient training, and a clear point of view on when it beats prompting.
- Background in marketplace, pricing, forecasting, or document extraction problems.
- Freight, logistics, or supply chain domain experience.
Why DAT: DAT is an award winning employer of choice, offers a hybrid work environment, and values integrity, mutual respect, open communication, and executing with excellence. The company has been an employer of choice in the Portland metropolitan area for four decades, and within one year of opening its Denver office, DAT was #26 on Built In Colorado’s 100 Best Places to Work In Colorado.
Washington pay transparency: For Washington-based candidates, DAT considers scope and responsibilities of the position, candidate work experience, education and training, core skills, internal equity, and market and business elements when extending an offer. The salary range for this role is provided above.
