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The pay range is $168,000.00 - $356,000.00 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.
JOIN TARGET AS A PRINCIPAL ENGINEER –AI PLATFORMS, FORWARD DEPLOYED ENGINEERING and AI BUILDER EXPERIENCE
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.
A role with Target’s AI Platform organization means the opportunity to build and evolve the foundational capabilities that enable teams across Target to develop, deploy, and operate AI-powered products and experiences at enterprise scale. Our teams build platforms, frameworks, and developer experiences that make advanced AI technologies accessible, reliable, and scalable for engineers across the organization.
As a Principal Engineer – AI Builder Experience and Forward Deployed Engineering, you will provide technical leadership for how engineering teams across Target build, integrate, deploy, and operate AI-powered applications. You will work at the intersection of AI platform engineering, developer experience, and forward deployed engineering, partnering directly with product and engineering teams to accelerate complex and high-value AI initiatives while shaping reusable capabilities for the broader engineering community.
You will help architect and deliver production-grade AI solutions, including agentic and LLM-powered applications, while establishing scalable patterns for model and agent integration, enterprise context, evaluation, observability, deployment, and production operations. You will bring deep platform expertise directly to engineering teams, helping them navigate complex technical challenges and successfully move AI solutions from experimentation into production.
A critical part of this role is translating real-world implementation experience into improvements to the AI platform. You will identify recurring challenges and patterns across forward deployed engagements and turn those learnings into reusable APIs, SDKs, frameworks, developer tooling, reference architectures, and self-service platform capabilities. Through this feedback loop, you will help reduce bespoke engineering and continuously improve the experience for the next generation of AI builders.
You will also provide technical leadership for the end-to-end AI developer experience, including onboarding, development, experimentation, testing, evaluation, debugging, deployment, and observability. You will partner closely with platform engineering, product, architecture, and application teams to influence platform roadmaps, establish engineering standards, and create reference implementations that enable teams to adopt AI capabilities effectively and responsibly at scale.
Given the pace of change in AI, you will stay current on emerging technologies and engineering approaches, evaluate and recommend new frameworks and tooling, publish learnings and reusable patterns, mentor senior engineers, and influence technical direction across the broader AI engineering ecosystem. We utilize Agile principles, follow best-practice software design, participate in code reviews, and create maintainable, well-tested codebases with relevant documentation.
Core responsibilities of this job are articulated within this job description. Job duties may change at any time due to business needs.
About you:
- MS or PhD in Computer Science, Engineering, Mathematics, Statistics, or another 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 in production environments
- Strong hands-on software engineering experience and demonstrated ability to architect and deliver production-grade systems
- Experience designing and delivering AI applications, including LLM-powered or agentic systems, and understanding of model integration, context and retrieval patterns, tool integration, evaluation, and production operations
- Experience building developer-facing capabilities such as APIs, SDKs, frameworks, libraries, developer tooling, templates, or self-service platform experiences
- Experience with modern AI engineering and platform technologies, including model APIs, agent development frameworks, evaluation and observability tooling, cloud platforms, containers, orchestration, CI/CD, and operational monitoring
- Strong understanding of distributed system design, platform architecture, scalability, reliability, performance optimization, security, and production deployment considerations
- Demonstrated ability to partner directly with engineering and product teams, understand complex implementation challenges, and translate those challenges into scalable technical solutions and reusable platform capabilities
- Ability to operate effectively across the full engineering lifecycle, from architecture and prototyping through implementation, production deployment, and operational maturity
- Ability to translate ambiguous business and engineering problems into clear technical approaches and create strong technical documentation, reference architectures, narratives, and recommendations for a range of audiences
- Excellent communication and influencing skills, with demonstrated ability to establish technical direction and drive alignment across engineering teams and organizational boundaries
- Proven ability to mentor and provide technical leadership to senior engineers while influencing architecture and engineering standards without direct authority
- Self-driven and results-oriented, with strong ownership, sound technical judgment, and the ability to balance near-term delivery with long-term platform scalability
- Collaborative team player with experience working effectively across functions, organizations, and geographies, and with a demonstrated commitment to continuous learning and knowledge sharing
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_FAmericans 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 $168K-$356K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 in Demand for This Role
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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($262K) sits 24% above the category median. Disclosed range: $168K to $356K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Target AI Hiring
Target has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist, MLOps Engineer. Based in Brooklyn Park, MN, US. Compensation range: $176K - $356K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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