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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.
JOIN TARGET AS A SR ENGINEER – CONTEXT INTELLIGENCE (AI PLATFORM)
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, Context Intelligence you will be part of a team building the enterprise context and knowledge foundation that enables AI applications and agents to access, retrieve and reason over trusted enterprise information.
You will operate at the intersection of AI engineering, information retrieval, search, knowledge systems, and distributed platform engineering. You will design and build scalable context services that enable semantic and hybrid retrieval, grounding, memory, enterprise knowledge access, and high-quality AI experiences.
A key part of this role is turning complex context and knowledge challenges into reusable platform capabilities. You will build retrieval services, memory systems, knowledge ingestion and indexing pipelines, context orchestration capabilities, and observability that can be adopted consistently across AI applications and agents.
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 Context Intelligence, you’ll take the lead as you:
- Design, build, and operate scalable retrieval and context services that enable AI applications and agents to access trusted enterprise knowledge.
- Build semantic, hybrid, and enterprise search capabilities using modern information retrieval techniques, embeddings, vector databases, indexing, and search infrastructure.
- Develop retrieval-augmented generation (RAG) and grounding patterns that improve the relevance, quality, and trustworthiness of AI responses.
- Build memory and context-management capabilities for conversational and agentic systems, including short- and long-term context and agent memory patterns.
- Design and implement enterprise knowledge ingestion, content indexing, metadata, synchronization, and connector capabilities that keep knowledge accessible and fresh.
- Build context orchestration services that select, assemble, and optimize the information supplied to AI applications and agents.
- Develop retrieval observability and diagnostics, including grounding quality, retrieval quality, search metrics, freshness, performance, and operational health.
- Apply strong distributed systems and platform engineering principles to build reliable, scalable, secure, and maintainable stateful services and indexing systems.
- Design and operate containerized services on Kubernetes and contribute to resilient, multi-region architectures for enterprise-scale context and knowledge workloads.
- Evaluate emerging retrieval, RAG, GraphRAG, knowledge graph, semantic technology, and enterprise search approaches through research and proof-of-concept activities.
- Lead code reviews, design reviews, testing, debugging, performance optimization, and architecture discussions while maintaining high engineering and operational standards.
- Partner with AI platform, application engineering, product, architecture, and data teams to understand context needs and translate them into reusable platform capabilities.
- Diagnose complex retrieval, indexing, grounding, memory, and production issues, identify systemic causes, and develop solutions that prevent repeat occurrences.
- Share technical patterns and best practices and help engineering teams successfully adopt Context Intelligence platform capabilities.
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, platform services, or distributed systems
- Experience with information retrieval, search architecture, RAG, embeddings, vector databases, or related context and knowledge technologies
- Strong proficiency in one or more modern programming languages and experience developing APIs, services, and data-intensive systems
- Experience building or integrating semantic search, hybrid search, retrieval services, indexing pipelines, or enterprise search capabilities
- Understanding of memory systems, context management, grounding, and retrieval patterns for LLM-powered or agentic applications
- Strong understanding of distributed systems, stateful services, scalability, reliability, performance, security, and production operations
- Experience with Kubernetes, containers, CI/CD, observability, testing, debugging, and operational monitoring
- Experience with search infrastructure, distributed indexing, metadata systems, knowledge ingestion, or enterprise data connectors
- Ability to take ambiguous requirements, understand the context needs of AI systems and translate them into concrete technical designs and working software
- Demonstrated ability to evaluate retrieval and system tradeoffs across relevance, latency, freshness, scale, reliability, and cost
- Strong problem-solving skills with the ability to diagnose complex retrieval, indexing, and operational issues and eliminate repeat occurrences
- Experience collaborating with platform, product, application engineering, architecture, and data teams across organizational boundaries
- Ability to communicate complex technical concepts clearly, influence design decisions, mentor engineers, and raise engineering standards within a team
- Maintains technical knowledge within areas of expertise and stays current with evolving retrieval, knowledge, AI, and platform 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.
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.
Application deadline is : 11/19/2026
Salary Context
This $98K-$176K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 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 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $98K to $176K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Target AI Hiring
Target has 10 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, AI Software Engineer, Data Scientist. Positions span Brooklyn Park, MN, US, Minneapolis, MN, US, Brooklyn Park, MN. Compensation range: $176K - $356K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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