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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.
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, you serve as a specialist in the engineering team that supports the Agentic Commerce Initiative. You help develop and gain insight in the application architecture. You can distill an abstract architecture into concrete design and influence the implementation. You show expertise in applying the 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 the architecture on your own but choose to influence your fellow engineers by proposing software designs, providing feedback on software designs and/or implementation. You show good problem\-solving skills and can help the team in triaging operational issues. You leverage your expertise in eliminating repeat occurrences.
About this Team:
Agentic Commerce is a forward\-looking engineering team at the forefront of building next\-generation commerce experiences powered by agentic technologies. We are redefining how guests shop by enabling seamless, intelligent interactions both on and off Target platforms.
Our team is responsible for building:
- Off\-platform commerce experiences across partners like Google, Microsoft, and OpenAI—meeting guests wherever they are.
- On\-platform Shopping Assistant experiences across Web, iOS, and Android—delivering conversational, agent\-driven shopping journeys.
We are developing foundational capabilities such as the Agentic Commerce Gateway, Shopping agents which fronts MCP servers, RAG solutions to power browse, checkout, and post\-purchase experiences. Additionally, we enable instant checkout through UCP (Universal Commerce Protocol) integrations and build intelligent digital agents that drive personalized shopping experiences.
As a Senior Engineer, you will play a critical role in shaping and delivering scalable, resilient, and intelligent commerce systems. You will lead the design and development of agentic commerce capabilities, drive technical strategy, and partner closely with internal and external stakeholders to expand Target’s digital ecosystem.
This role requires strong technical leadership, systems thinking, and the ability to operate in a fast\-evolving space with emerging protocols and AI\-driven experiences.
Tech Stack:
- Languages \& Frameworks: Kotlin, Micronaut, Python, lang graph
- Testing: Kotest
- Observability: Grafana, Kibana
- CI/CD: Vela (continuous integration and deployment)
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
- Demonstrates strong domain\-specific knowledge regarding Target’s technology capabilities, and key competitors’ products and differentiating features
- Working knowledge on package\-specific configuration and deployment along with ability to build custom solutions
- Designs new testing methods and resolves routine and non\-routine technical issues with minimal assistance
- Demonstrates strong proficiency in core development platforms within own domain
- Builds strong commitment within the team to support the appropriate team priorities
- Clearly communicates Agile concepts to partners within product team
- Demonstrates a solid understanding of the impact of own work on the team and/or guests
- Writes and organizes code using multiple computer languages, including distributed programming and understand different frameworks and paradigm
- Delivers high\-performance, scalable, repeatable, and secure deliverables with broad impact (high throughput and low latency)
- Influences and applies data standards, policies, and procedures
- Maintains technical knowledge within areas of expertise
- Stays current with new and evolving technologies via formal training and self\-directed education
- Understanding of Agentic Commerce concepts, including MCP, ACP, and agent\-driven application design.
- Experience building high\-performance, scalable services, preferably using Kotlin and Micronaut.
- Proven ability to design and implement high\-throughput APIs, capable of supporting 10K\+ TPS with p99 latencies under 100 ms.
- Experience creating event\-driven architectures and systems that scale dynamically based on load.
- Ability to work across functions and collaborate with multiple stakeholders in driving complex initiatives.
- Familiarity with observability tools (Grafana, Kibana) and modern automated CI/CD pipelines.
- Strong problem\-solving skills, engineering rigor, and a passion for building innovative commerce technologies.
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 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($137K) sits 24% below the category median. Disclosed range: $98K to $176K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Target AI Hiring
Target has 7 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer, Data Scientist. Based in Brooklyn Park, MN, US. Compensation range: $135K - $303K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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