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About This Role
The pay range is $98,000\.00 \- $176,000\.00
Pay is based on several factors which vary based on position. These include labor markets and in some instances may include education, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves. Other benefits for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation. Find competitive benefits from financial and education to well\-being and beyond at https://corporate.target.com/careers/benefits.
Senior AI Engineer (Backend Platform) –CircleCardAccount Services (CAS)
About Us
Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and inclusive culture.
As a Senior AI Engineer, you'll combine deep software engineering expertise with modern AI application development to build intelligent, scalable, and highly reliable systems. You'll serve as a technical leader, shaping application architecture, influencing implementation decisions, and establishing engineering best practices.
You are equally comfortable designing distributed services and applying Generative AI technologies to solve complex engineering and business problems. You leverage strong software engineering fundamentals to build production\-grade AI capabilities that are secure, observable, scalable, and cost\-efficient.
About the Team:CircleCardAccount Services (CAS)
The CircleCard Account Services (CAS) team builds and operates the enterprise API platform powering Target's CircleCard ecosystem. Our platform serves as the central gateway for CircleCard account management, new account booking, payments, servicing, compliance processing, and partner integrations.
CAS owns a mission\-critical platform that enables secure, high\-volume financial and non\-financial operations across Target. Our services power nearly every aspect of the CircleCard account lifecycle and integrate with numerous enterprise systems.
Current initiatives include:
- Building AI\-ready platform capabilities and MCP\-based tools and services.
- Expanding shared platform capabilities, including distributed caching and reusable platform services.
- Improving API performance, scalability, resiliency, and observability to support significant traffic growth.
- Building AI\-powered engineering productivity capabilities.
- Automating engineering and operational workflows through intelligent AI agents.
As AI becomes a foundational capability across Target, CAS is investing in intelligent engineering platforms and AI\-powered solutions that improve developer productivity, automate operational workflows, and enhance platform reliability.
What You'll Do
AI Engineering
- Leverage AI\-assisted software development tools throughout the software development lifecycle, including solution design, implementation, testing, debugging, documentation, and code reviews.
- Collaborate effectively with AI coding assistants and enterprise AI platforms to improve engineering productivity while maintaining high standards for code quality, security, architecture, and maintainability.
- Apply sound engineering judgment to validate AI\-generated code and technical recommendations through testing, peer reviews, and established software engineering practices.
- Design and build production AI\-powered applications using Target\-approved AI platforms, frameworks, and Large Language Models (LLMs) to solve engineering and business problems.
- Design, build, and deploy production\-grade autonomous and agentic AI systems capable of reasoning, planning, tool orchestration, memory management, and multi\-step workflow execution.
- Develop domain\-specific AI agents and multi\-agent workflows using modern orchestration frameworks to automate engineering processes and enhance retail and enterprise experiences.
- Design and implement prompt engineering and Context Engineering strategies to improve application quality, reliability, and relevance.
- Build Retrieval\-Augmented Generation (RAG) solutions leveraging enterprise knowledge sources, vector search technologies, and semantic retrieval.
- Optimize AI applications for response quality, latency, throughput, token utilization, and inference cost.
- Implement AI evaluation frameworks and apply responsible AI, security, privacy, governance, and observability best practices.
- Build reusable AI components, frameworks, libraries, workflows, and engineering accelerators that improve developer productivity.
- Evaluate emerging AI capabilities, lead proof\-of\-concept initiatives, and recommend adoption strategies that deliver measurable business value.
- Partner across engineering teams to integrate AI capabilities into products, platforms, and software development workflows.
Services Engineering
- Design, develop, and maintain highly scalable AI\-ready services using Kotlin, Java, and Micronaut.
- Build secure, high\-performance RESTful APIs and event\-driven services supporting high\-volume retail and financial workloads.
- Design distributed systems using Kafka, asynchronous messaging, distributed caching, and cloud\-native architecture patterns.
- Design and optimize PostgreSQL databases, including data modeling, indexing, query optimization, and transactional integrity.
- Build resilient, highly available services with strong fault tolerance, scalability, and operational excellence.
- Lead technical design discussions, architecture reviews, code reviews, and implementation planning.
- Improve platform observability using OpenTelemetry, distributed tracing, metrics, logging, and production monitoring.
- Continuously improve application performance, scalability, resiliency, observability, and operational health.
- Partner closely with Product Management, UX, Architecture, Infrastructure, Data, and business stakeholders to deliver customer\-focused solutions.
About You
Required Qualifications
- Bachelor's degree or equivalent practical experience.
- 5\+ years of software engineering experience building enterprise\-scale distributed applications and services.
AI Engineering
- Hands\-on experience designing and deploying production AI applications, AI\-powered tools, or agentic systems using modern Large Language Models (LLMs).
- Experience building autonomous or multi\-agent workflows using technologies such as Model Context Protocol (MCP), LangGraph, LangChain, CrewAI, Google ADK, Semantic Kernel, or equivalent orchestration frameworks.
- Strong understanding of AI application architecture, including Prompt Engineering, Context Engineering, Retrieval\-Augmented Generation (RAG), tool calling, memory management, orchestration patterns, and AI evaluation.
- Experience optimizing AI applications for quality, latency, token efficiency, and inference cost while implementing responsible AI, governance, and observability practices.
- Proficiency in Python for AI application development, automation, or AI tooling.
Services Engineering
- Expert\-level proficiency in Kotlin and Java, with experience building microservices using Micronaut and/or Spring Boot.
- Strong experience designing distributed systems, RESTful APIs, Kafka\-based event\-driven architectures, PostgreSQL, Redis, Docker, Kubernetes, and modern CI/CD pipelines.
- Experience building highly available, resilient, secure, and observable production services.
Leadership \& Collaboration
- Strong communication, collaboration, and technical leadership skills with experience mentoring engineers, leading design discussions, and driving engineering best practices.
- Passion for applying emerging AI technologies to solve complex engineering and business challenges.
Why Join Us
CircleCard Account Services sits at the heart of one of Target's most critical financial platforms. Every day, millions of guests and internal systems rely on the services we build.
As a Senior AI Engineer, you'll help shape the future of enterprise software engineering by combining modern AI capabilities with large\-scale distributed systems. You'll build intelligent services, reusable engineering platforms, and production\-grade agentic AI capabilities that improve developer productivity, strengthen operational excellence, and deliver exceptional experiences for Target's guests and team members.
This position will operate as a Hybrid/Flex for Your Day work arrangement based on Target’s needs. A Hybrid/Flex for Your Day work arrangement means the team member’s core role will need to be performed both onsite at the Target HQ MN location the role is assigned to and virtually, depending upon what your role, team and tasks require for that day. Work duties cannot be performed outside of the country of the primary work location, unless otherwise prescribed by Target. Click here if you are curious to learn more about Minnesota.
Benefits Eligibility
Please paste this url into your preferred browser to learn about benefits eligibility for this role: https://tgt.biz/BenefitsForYou\_DAmericans with Disabilities Act (ADA)
In compliance with state and federal laws, Target will make reasonable accommodations for applicants with disabilities. If a reasonable accommodation is needed to participate in the job application or interview process, please reach out to [email protected]. Non\-accommodation\-related requests, such as application follow\-ups or technical issues, will not be addressed through this channel.
Salary Context
This $98K-$176K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Target, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($137K) sits 37% below the category median. Disclosed range: $98K to $176K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Target AI Hiring
Target has 5 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Based in Brooklyn Park, MN, US. Compensation range: $176K - $356K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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