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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.
JOIN TARGET AS A SR ENGINEER – AI PLATFORMS – FORWARD DEPLOYED ENGINEERING
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.
As a Senior Engineer – AI Platforms, Forward Deployed Engineering (FDE), you will be part of a team focused on accelerating the adoption of Target's enterprise AI platforms by working directly with engineering and product teams to solve complex, high-value AI problems.
You will operate at the intersection of AI engineering, platform engineering, and product development, bringing deep technical expertise directly to teams building AI-powered experiences. You will help teams move from experimentation to production by designing and implementing solutions, integrating enterprise AI platform capabilities, and establishing scalable engineering patterns.
A key part of this role is learning from real-world implementations and using those insights to improve the broader AI platform and builder experience. You will identify recurring technical challenges and work with platform teams to turn successful implementation patterns into reusable components, APIs, SDKs, frameworks, tooling, reference implementations, and self-service capabilities.
As a Senior Engineer, you serve as a specialist in the engineering team that supports the product. You help develop and gain insight into application architecture. You can distill an abstract architecture into concrete design and influence the implementation. You demonstrate expertise in applying appropriate software engineering patterns to build robust and scalable systems. You are an expert in programming and apply your skills in developing the product. You have the skills to design and implement architecture independently while influencing fellow engineers through software designs, implementation guidance, code reviews, and technical mentorship.
Use your skills, experience, and talents to be part of groundbreaking thinking and visionary goals. As a Sr. Engineer within Forward Deployed Engineering, you’ll take the lead as you:
- Partner directly with engineering and product teams to design, build, and productionize AI-powered applications and experiences using Target's enterprise AI platform.
- Develop production-grade solutions using generative AI, LLMs, agentic architectures, enterprise context and retrieval, model APIs, tools, and platform services.
- Rapidly understand ambiguous business and engineering problems, translate them into technical designs, and build working solutions that demonstrate measurable value.
- Lead technical implementation across the lifecycle—from exploration and prototyping through architecture, development, evaluation, deployment, observability, and production operations.
- Apply strong software engineering and distributed systems principles to ensure AI solutions are scalable, reliable, secure, maintainable, and production-ready.
- Work closely with AI Platform teams to integrate existing platform capabilities and identify gaps encountered while building real-world applications.
- Identify patterns across FDE engagements and help convert repeatable solutions into reusable platform capabilities, APIs, SDKs, libraries, frameworks, templates, and reference architectures.
- Improve the AI builder experience by reducing friction across development, experimentation, testing, evaluation, debugging, deployment, and production operations.
- Build reference implementations and examples that demonstrate effective patterns for adopting enterprise AI capabilities.
- Evaluate emerging AI technologies, frameworks, models, and developer tooling through research and proof-of-concept activities and recommend appropriate approaches based on scalability, maintainability, security, and cost.
- Lead code reviews, design reviews, testing, debugging, and architecture discussions while maintaining high engineering quality and operational standards.
- Diagnose complex production and integration issues, identify systemic causes, and develop solutions that prevent repeat occurrences.
- Collaborate across platform engineering, application engineering, product, architecture, data science, and other teams to deliver solutions spanning organizational and technical boundaries.
- Share technical learnings, patterns, and best practices from FDE engagements with the broader engineering community and help other engineers successfully adopt them.
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.
- 5+ years of software development experience with strong hands-on engineering and programming skills.
- Strong experience designing, building, testing, deploying, and operating production-grade applications, services, or distributed systems.
- Experience building AI-enabled applications and working with technologies such as LLMs, model APIs, agentic frameworks, retrieval/context patterns, tool integration, or other modern AI capabilities.
- Strong proficiency in one or more modern programming languages, with experience developing services and applications using modern frameworks and engineering practices.
- Experience designing and consuming APIs, SDKs, libraries, frameworks, or platform capabilities.
- Strong understanding of software architecture, distributed systems, APIs, scalability, reliability, performance, security, and production operations.
- Experience with cloud-native development, containers, orchestration, CI/CD, observability, testing, debugging, and operational monitoring.
- Ability to take ambiguous requirements, rapidly understand the problem space, and translate them into concrete technical designs and working software.
- Ability to work across the full engineering lifecycle—from rapid prototyping and experimentation to hardened production systems.
- Demonstrated ability to evaluate technical tradeoffs and balance speed of delivery with scalability, maintainability, operational excellence, and long-term platform needs.
- Strong problem-solving skills with the ability to diagnose complex technical and operational issues and eliminate repeat occurrences.
- Experience collaborating directly with product teams and engineers and working effectively across organizational and technical boundaries.
- Ability to communicate complex technical concepts clearly and influence design and implementation decisions through technical expertise.
- Demonstrated ability to mentor engineers, provide technical guidance, and raise engineering standards within a team.
- Maintains technical knowledge within areas of expertise and stays current with rapidly evolving AI and software engineering technologies through formal training and self-directed learning.
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.
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_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: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $98K to $176K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Target AI Hiring
Target has 7 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer, AI Software Engineer. Positions span Brooklyn Park, MN, Brooklyn Park, MN, US, Minneapolis, MN, US. Compensation range: $176K - $356K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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