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Full Stack Data Scientist

Job in Seattle, King County, Washington, 98127, USA
Listing for: Boeing
Full Time position
Listed on 2026-10-15
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Cloud Computing: Infrastructure & Operations
Salary/Wage Range or Industry Benchmark: 138000 - 186000 USD Yearly USD 138000.00 186000.00 YEAR
Job Description & How to Apply Below
Job Description

At Boeing, we innovate and collaborate to make the world a better place. We’re committed to fostering an environment for every teammate that’s welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us.

At Boeing, we innovate and collaborate to make the world better. Boeing Defense, Space & Security (BDS) is seeking a Full Stack Data Scientist to join the Business Transformation organization in support of modernizing how BDS operates through artificial intelligence, machine learning, and advanced digital capabilities. This role will help build and scale the technical foundations that enable secure, reliable, and production‑ready AI/ML solutions across the enterprise.

This position is ideal for a data scientist who brings a blend of data science, data engineering, MLOps, platform engineering, Dev Ops, and site reliability engineering (SRE) experience. The selected candidate will help design, implement, secure, and operate the infrastructure and platform services that support data pipelines, model training, model deployment, observability, and AI application delivery. The role will work across on‑premises, hybrid, and cloud‑based environments, with a strong emphasis on enterprise‑grade security, resiliency, traceability, and operational excellence.

The full stack data scientist in this role will partner closely with data scientists, software engineers, data engineers, cybersecurity teams, infrastructure teams, and business stakeholders to accelerate adoption of AI/ML capabilities within BDS. This includes enabling both traditional machine learning workflows and emerging generative AI use cases, including support for on‑premises model serving, retrieval‑augmented generation architectures, and modern model lifecycle management practices.

The role requires strong systems thinking and the ability to translate business and mission needs into scalable platform solutions.

The successful candidate will be expected to contribute both strategically and hands‑on. They will help define best practices, build reusable patterns, automate infrastructure and delivery workflows, improve platform reliability, and support secure deployment of mission‑relevant AI/ML systems. Depending on level, this role may also provide technical leadership, influence architecture decisions, and mentor other data scientists across the organization.

This position offers the opportunity to shape the future of AI/ML enablement within Boeing BDS by building the secure and modern technical ecosystem needed to support transformation at scale.

Key Responsibilities
  • Design, build, deploy, and operate secure, scalable AI/ML platform capabilities for Boeing BDS users and programs
  • Develop and maintain data pipelines, workflow orchestration solutions, and data integration services that support analytics and machine learning use cases
  • Build and support MLOps workflows for model training, experiment tracking, model registry, deployment, inference, monitoring, and lifecycle management
  • Engineer and operate infrastructure for traditional machine learning and generative AI workloads, including on‑premises and hybrid model serving environments
  • Deploy, administer, and optimize containerized applications and platform services using Kubernetes and associated ecosystem tools
  • Create and maintain Helm charts, deployment templates, automation scripts, and reusable platform patterns for AI/ML applications
  • Implement CI/CD and Git Ops workflows to support secure, repeatable, and observable software and model delivery processes
  • Configure and support observability capabilities including metrics, logs, traces, dashboards, alerting, and reliability reporting
  • Apply Dev Sec Ops  principles across…
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