below marketmost similar roles pay hereabove market
This role pays more than
81%
of similar roles. Most pay
$169,550–$250,321
— the blue band above.
At the midpoint, this role pays about
$262k
versus about
$210k
for comparable roles.
Based on 240 similar postings.
Employer
About Target
Target Corporation is a large-format general merchandise and grocery retailer offering a wide assortment of everyday essentials, apparel, home goods, and electronics through stores and online. Industry: General Merchandise Retail
Target currently has
72 open roles
on FindRole.
Listed pay typically runs
$98,000–$176,000
across 55 roles with salary data.
JOB TITLE: Principal Engineer - AI Platform (Forward Deployed Engineering and AI Builder Experience)
As a Principal Engineer within the AI Platform organization, you will provide technical leadership to help engineering teams build, integrate, and operate AI-powered applications. You will work at the intersection of platform engineering and forward deployed engineering, architecting production-grade solutions including agentic and LLM-powered applications. Your daily work involves translating real-world implementation challenges into reusable APIs, SDKs, frameworks, and developer tooling to improve the end-to-end AI developer experience. You will utilize model APIs, agent development frameworks, evaluation and observability tooling, cloud platforms, containers, orchestration, and CI/CD. The role focuses on solving the technical problem of moving AI solutions from experimentation into production by establishing scalable patterns for model integration, enterprise context, and production operations across the broader engineering ecosystem.
What does a Engineer earn in Minnesota?
Median $137000 from 31 postings across 4 companies.
Architect and deliver production-grade AI solutions, including agentic and LLM-powered applications.
Establish scalable patterns for model integration, enterprise context, evaluation, observability, and production operations.
Translate real-world implementation challenges into reusable APIs, SDKs, frameworks, and developer tooling.
Provide technical leadership for the end-to-end AI developer experience, including onboarding, testing, and debugging.
Partner with product and engineering teams to influence platform roadmaps and establish engineering standards.
Create reference implementations and documentation to enable teams to adopt AI capabilities at scale.
Mentor senior engineers and influence technical direction across the broader AI engineering ecosystem.
Stay current on emerging AI technologies to evaluate and recommend new frameworks and tooling.
What we're looking for
MS or PhD in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative discipline or equivalent industry experience.
8 plus years of experience designing, building, and operating large-scale software platforms, developer platforms, AI/ML systems, or distributed systems.
Strong hands-on software engineering experience with the ability to architect and deliver production-grade systems.
Experience designing and delivering AI applications, including LLM-powered or agentic systems, with knowledge of model integration and retrieval patterns.
Experience building developer-facing capabilities such as APIs, SDKs, frameworks, libraries, and self-service platform experiences.
Proficiency with modern AI engineering technologies, including model APIs, agent frameworks, evaluation tools, cloud platforms, containers, and CI/CD.
Strong understanding of distributed system design, platform architecture, scalability, reliability, performance optimization, and security.
Proven ability to mentor senior engineers and provide technical leadership to drive alignment across engineering teams.
MLOps
Kubernetes
Vertex AI
Kubeflow
MLflow
Terraform
CI/CD
Infrastructure as Code
GitOps
Generative AI
Distributed Systems
Service Mesh
GPU Infrastructure
Model Governance
Observability
Cloud-Native
Machine Learning
Platform Engineering
Remote (Palo Alto, CA)+19 days ago
$242,634–$499,541
Generative AI
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Distributed Systems
C++
Java
Rust
AI/ML Infrastructure
Model Training
High-Performance Inference
Data Orchestration
Feature Stores
Multimodal Data Management
Online Inference
Python
Go
Java
C++
Kubernetes
OCI
Distributed Systems
Machine Learning
LLMs
PyTorch
TensorFlow
Hugging Face Transformers
CI/CD
Microservices
Event-Driven Systems
Infrastructure Automation
Service Mesh
Prompt Engineering
Distributed Systems
Backend Systems
High-Performance Computing
Data Governance
RBAC
API
Databricks
RAG
Vector Stores
LLM
DLP
Data Masking
Schema Design
Audit Logging
SaaS Integration
MLOps
Generative AI
Agentic AI
Private Cloud
GPU
CPU
High Performance Storage
High Speed Networking
Predictive ML
Model Lifecycle Management
Data Center Design
Liquid Cooling
Observability
RFI
RFP