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About This Role
The pay range is $132,000\.00 \- $238,000\.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.
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 culture. Learn more about Target here. Target is one of the world’s most recognized brands and one of America’s leading retailers. But behind the brand our guests love, is a culture of continual innovation and right now, we are up to big things! Target’s security team is a place where innovation happens daily. Interested in a culture that combines ongoing learning, engineering excellence, and stellar outcomes? We are too – that’s why we work here. Join our team to improve Target’s security and move the business forward.
As a Lead Engineer on the Security Architecture team, you’ll collaborate with technical and leadership teams across Target Tech to ensure that systems are designed and built securely. You will identify relevant security risks, develop appropriate mitigation strategies, integrate security functions and controls into the overall system architecture, evaluate and provide feedback on proposed architectures, and design secure architecture. This role is highly technical, and you bring a deep understanding of security risks, controls, mitigations, and standards to a collaborative and advisory role, helping the rest of the enterprise as new platforms and systems are built, and as existing ones are modified over time.
Beyond the deep expertise, you have great interpersonal skills: our Security Architects are called upon to collaborate across the enterprise and have exceptional communication skills that enable open and cooperative partnerships.
Expect to:
- Collaborate with system designers to integrate security requirements into the design phase of IT systems
- Develop and maintain security architecture documentation, including security models, frameworks, and diagrams
- Ensure that security architecture aligns with the organization’s business objectives and regulatory requirements
- Understand security risks in order to identify potential vulnerabilities and threats
- Develop risk mitigation strategies and recommend appropriate security controls
- Design and implement security solutions, including firewalls, encryption protocols, and access control mechanisms
- Collaborate with development and operations teams to ensure secure creation and deployment of IT systems
- Provide guidance on secure coding practices
- Prioritize driving highly impactful changes that improve the business
- Conduct full\-stack architecture reviews of products and platforms
- Provide expertise on information security for complex systems and applications in cloud and on\-prem environments
- Design security reference architectures and create implementation/configuration guides
- Provide expertise on creation and implementation of security controls with an emphasis on cloud technologies
- Efficiently assess and communicate risk accurately while negotiating priorities with cross\-domain stakeholders
- Collaborate with engineering teams to perform advanced security analysis on complex cloud systems, identifying gaps while contributing to design solutions and security requirements
Core responsibilities of this job are described within this job description. Job duties may change at any time due to business needs.
About You:
- 4\-year degree OR equivalent experience
- Polyglot programmer comfortable in many languages across different platforms
- 7\+ years of hands\-on experience in technology, with extensive knowledge of cybersecurity domains including Information Protection, AI Security and/or Cloud Security (GCP strongly preferred)
- Demonstrated curiosity and ability to learn
- Expertise in containerization technologies and tools
- Solid understanding of genAI/ML
- Understanding of prompt injection and its various styles (direct, indirect, RAG poisoning, etc.) / Familiarity with OWSAP top ten for LLMs
- Understanding of MCP auth patterns including dynamic client registration
- Knowledge in RAG authorization patterns
- Experience mitigating the security risks of local coding agents.
- Seeks out cross\-team collaboration opportunities
- Stays current on relevant technologies with self\-directed learning
- Excellent written and verbal interpersonal skills with strong presentation abilities
- Proven history of effectively utilizing a variety of security tools and technologies across diverse environments. The ideal candidate will not be limited to specific vendors or solutions but will possess the technical depth to comprehend and implement an end\-to\-end solution that aligns with our reference security architecture's requirements
- Good understanding of security management workflows in large enterprise organizations and complex environments
- Has a good understanding of the current threat landscape and the challenges that most organizations are facing
- Knowledge of security frameworks, standards, and best practices (e.g., NIST, ISO/IEC 27001\)
- Strong understanding of network security, cryptography, and secure software development
- Experience with security technologies, such as firewalls, IDS/IPS, SIEM, and DLP
- Excellent analytical, problem\-solving, and communication skills
Preferred:
- GCP native security product experience (VPC Service Controls, Organization constraints, Cloud Armor, NextGen Firewall etc.)
- Experience with Google SCC or Wiz is a plus
- Vertex AI experience
- AWS or Azure experience
This position may be considered for a Remote or Hybrid (known internally at Target as "Flex for Your Day") work arrangement based on Target's needs. A Remote work arrangement means the team member works full\-time from home or an alternate location that's not a Target location, does not have a desk at a Target location and may travel to HQ up to 4 times a year. A Hybrid/Flex for Your Day work arrangement means the team member's core role may be performed either remote or onsite at a Target location 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.Benefits Eligibility
Please paste this url into your preferred browser to learn about benefits eligibility for this role: https://tgt.biz/BenefitsForYou\_EAmericans 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 candidate.accommodations@HRHelp.Target.com. Non\-accommodation\-related requests, such as application follow\-ups or technical issues, will not be addressed through this channel.
Application deadline is : 09/04/2026
Salary Context
This $132K-$238K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($185K) sits 14% below the category median. Disclosed range: $132K to $238K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Target AI Hiring
Target has 9 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, Data Scientist. Positions span Minneapolis, MN, US, Brooklyn Park, MN, US, Sunnyvale, CA, US. Compensation range: $135K - $303K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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