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About This Role
Software Engineer II \- AI Foundations
As the Software Engineer II, you will play a key role in delivering software products for Best Buy’s AI Foundation team. You will work with Product Managers, User Experience Designers, Architects and other team members to modernize and build products that align to the product’s team vision and strategy. The engineering of these products will leverage multi\-cloud platforms, human centered design, Agile and DevOps principles to deliver industry leading solutions at high velocity with unrivaled quality. You will work on a team of talented software engineering professionals, leading by example, and mentoring others. All of this will be done by embracing inclusive behaviors, working through ambiguity, demonstrating a strong attention to detail, and thriving in a fast\-paced environment.
The Cloud AI Foundation team accelerates enterprise\-wide adoption of data and AI across Advanced Analytics, Product, and Sales \& Operations by delivering scalable data platforms, self\-service tools, and measurement capabilities that support continued growth. This role helps with building core AI and Machine Learning platform features and capabilities.
This role is hybrid, which means you will be required to work some days at our Best Buy office in Richfield, Minnesota, and some days virtually from home or another non\-Best Buy location. The recruiter or hiring manager will provide more details during the hiring process.
What you’ll do
- Deliver code features, system components, and releases
- Ensure quality delivery of features and systems
- Communicate effectively both verbally and written with various business partners.
- Participate in production support
- Improve knowledge and understanding of core concepts
Basic qualifications
- 2\+ years of relevant experience with a Bachelor's degree OR equivalent relevant professional experience
- 2\+ years of experience deploying, managing, and troubleshooting distributed workloads on Kubernetes.
- 3\+ years of hands\-on experience with at least one major cloud platform such as GCP, AWS, or Azure.
- 1\+ years of experience optimizing and right\-sizing cloud infrastructure for AI/ML workloads to improve performance, scalability, throughput, and cost efficiency.
Preferred qualifications
- Bachelor's degree in IT, Computer Science, Engineering, or related field
- 3\+ years of relevant professional experience
- Hands\-on experience with cloud\-native AI/ML services such as Vertex AI, Amazon SageMaker, or Azure Machine Learning
- Experience designing, deploying, and maintaining scalable, high\-performance model serving platforms
- Experience building and maintaining CI/CD and MLOps workflows using tools such as GitHub Actions and ArgoCD
- Proficiency with Infrastructure as Code tools such as Terraform and automation using Python
- Familiarity with ML frameworks and optimization technologies such as PyTorch, ONNX, and TensorRT
- Strong communication and collaboration skills with the ability to work effectively across engineering teams and stakeholders
- Relevant cloud or AI certifications, especially within Google Cloud or related platforms
What’s in it for you
We’re committed to helping our people thrive at work and at home. We offer generous benefits that address your total well\-being and provide support as you need it, especially key moments in your life.
Our benefits include:
- Competitive pay
- Generous employee discount
- Physical and mental well\-being support
Best Buy provides different types of leaves of absence (LOA) and potential pay sources to employees based on eligibility. The length of your LOA depends on your situation, where you live, your full\-time or part\-time employment status, and federal and state regulations. Intermittent or reduced\-schedule leave is also available for certain medical or family care leaves. Paid time off (vacation or PTO) is offered to full\-time and part\-time employees based on work location, employment status, salary or hourly status (exempt/non\-exempt), and years of continued or bridged service.
Certain roles, where market norms demand it, are eligible for various forms of incentive pay to drive performance and offer recognition for achieving financial and strategic results. For more information about our incentive pay plans, including eligibility, please refer to our Incentive Programs Summary.
For more information about benefits, LOA and paid time off, please refer to our Benefits Guide.
About us
As part of the Best Buy team, you’ll help us fulfill our purpose to enrich lives through technology. We bring that to life every day by humanizing and personalizing tech solutions for every stage of life — in our stores, online and in customers’ homes.
Our culture is built on deeply supporting and valuing our amazing employees who make it all possible. We’re committed to being a great place to work, where you can unlock unique career possibilities. Above all, we aim to provide a place where you can bring your full, authentic self to work now and into the future. Tomorrow works here.™
Best Buy is an equal opportunity employer.
Position Type: Full time
Salary Context
This $93K-$166K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Best Buy, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($130K) sits 30% below the category median. Disclosed range: $93K to $166K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Best Buy AI Hiring
Best Buy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US. Compensation range: $166K - $166K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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