Principal Engineer – AI/ML Platform

Target

Principal Engineer leading architecture and strategy for AI/ML platform at Target. Collaborating across teams to build scalable machine learning capabilities.

Posted 7/15/2026full-timeBrooklyn Park • California, Minnesota • 🇺🇸 United StatesLead💰 $168,000 - $356,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and delivering large-scale cloud-native platforms and enterprise machine learning operations, with a strong focus on model governance, observability, and responsible AI practices. Proven ability to mentor engineers and influence technical direction while collaborating with cross-functional teams to enhance platform capabilities.

Highest-signal resume keywords

Cloud-Native Platform DesignMachine Learning Operations (MLOps)Kubernetes-Based Platform EngineeringModel Governance and ObservabilityTechnical Strategy Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning Lifecycle ManagementDistributed Systems DesignEnterprise Machine Learning PlatformsCI/CD Best PracticesInfrastructure as CodeFeature ManagementGPU InfrastructureLarge-Scale Inference PlatformsTerraformGitOps

Soft Skills

Excellent CommunicationInfluencing SkillsMentoring

Tools & Technologies

Vertex AIKubeflowMLflowService Mesh TechnologiesPlatform Automation

Certifications & Qualifications

MS in Computer ScienceMS in EngineeringMS in Mathematics

Industry Keywords

Cloud-Native TechnologiesResponsible AIGenerative AI PlatformsModel Deployment StrategiesObservability

Tech Stack

Tools & technologies

CloudDistributed SystemsKubernetesTerraform

About the role

Key responsibilities & impact

Define the long-term technical strategy and architecture for the enterprise ML Operations Platform.
Design scalable, secure, and resilient cloud-native platforms supporting machine learning workloads.
Establish best practices for model development, deployment, monitoring, and lifecycle management.
Lead architecture for enterprise machine learning infrastructure supporting batch, streaming, and real-time inference.
Drive adoption of cloud-native technologies, Kubernetes, and modern platform engineering practices.
Define standards for model governance, observability, reliability, explainability, and responsible AI.
Partner with infrastructure, security, and engineering teams to improve platform scalability, performance, and operational efficiency.
Evaluate emerging technologies and recommend architectural approaches that improve platform capabilities.
Mentor engineers and influence technical direction across multiple engineering organizations.

Requirements

What you’ll need

MS in Computer Science, Engineering, Mathematics, or related technical field with relevant software engineering experience
Extensive experience designing and delivering large-scale cloud-native platforms or distributed systems
Deep experience building and operating enterprise machine learning platforms and MLOps capabilities
Strong understanding of machine learning lifecycle management, deployment strategies, observability and production operations
Demonstrated experience with machine learning platforms and tooling such as Vertex AI, Kubeflow, MLflow, and/or equivalent technologies
Experience building developer platforms or internal platform products
Experience with distributed training, GPU infrastructure, and large-scale inference platforms
Experience with feature management, model governance, and responsible AI practices.
Familiarity with Generative AI platforms and infrastructure supporting foundation model workloads
Experience with Terraform, GitOps, service mesh technologies, and platform automation
Experience mentoring senior engineers and leading enterprise-scale modernization initiatives
Expertise designing Kubernetes-based platforms supporting AI and machine learning workloads
Strong understanding of software engineering best practices including CI/CD, infrastructure as code, observability, testing, and automation
Experience defining technical strategy, architectural standards and engineering best practices across multiple teams
Excellent communication and influencing skills with the ability to communicate complex technical concepts to engineering and business leaders.

Benefits

Comp & perks

comprehensive health benefits and programs including medical, vision, dental, life insurance
401(k)
employee discount
short term disability
long term disability
paid sick leave
paid national holidays
paid vacation