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About This Role
Position Purpose:
The Discovery Intent and Guidance (DIG) team is a specialized engineering and product unit within the broader Discovery and Recommendations Platform. We are focused on building the core architectural and machine learning capabilities that power the digital product recommendation journey across all customer touchpoints. Our ultimate goal is to create a seamless, cohesive, and highly explainable product discovery experience for our users. We are seeking a highly skilled and highly motivated Senior Software Engineer to join the DIG team. In this role, you will play a critical part in designing, building, and modernizing the systems that drive our recommendation engines. You will work at the intersection of core backend engineering, data processing, and cutting\-edge artificial intelligence, helping to integrate large language models (LLMs) and agentic workflows into our product discovery ecosystem. As a senior member of the team, you will also be expected to lead system design initiatives, mentor junior engineers, and drive best practices in scalable architecture.
Key Responsibilities:
50% Delivery and Execution \- Develops, tests, deploys, and maintains software, with a clear understanding of the value the software is to provide; Takes on new opportunities and tough challenges with a sense of urgency, high energy and enthusiasm; Consistently achieves results, even under tough circumstances; Develops test suites (functional, destructive, etc) to enable success, rapid deployment of code to production; Takes a broad view when approaching issues; using a global lens
20% Learns and Grows \- Learns through successful and failed experiment when tackling new problems; Actively seeks ways to grow and be challenged using both formal and informal development channels
20% Plans and Aligns \- Collaborates with other team members in agile processes; Creates new and better ways for the organization to be successful; Works the Product Team to ensure user stories are valuable, developer ready, easy to understand and testable; Delivers multi\-mode communications that convey a clear understanding of the unique needs of different audiences; Adapts approach and demeanor in real time to match the shifting demands of different situations; Relates openly and comfortably with diverse groups of people
10% Supports and Enables \- Helps grow junior engineers by providing guidance on modern software development frameworks, and leading technical discussions
Direct Manager/Direct Reports:
This position typically reports to Software Engineer Manager or Sr. Manager
This position has 0 Direct Reports
Travel Requirements:
No travel required.
Physical Requirements:
Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.
Working Conditions:
Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.
Minimum Qualifications:
Must be eighteen years of age or older.
Must be legally permitted to work in the United States.
Preferred Qualifications:
3\-5 years of experience
Languages — Strong in at least one of: Java, Scala, Python
Microservices — REST APIs, gRPC, event\-driven architecture (Pub/Sub, Cloud Tasks)
System Design — Distributed systems, scalability, fault tolerance, API gateway patterns
GCP Core — Compute Engine, GKE, Cloud Run, Cloud Functions, IAM
BigQuery — Advanced SQL, partitioning, clustering, cost optimization
Pipelines — Dataflow (Apache Beam), Dataproc (Spark), batch and streaming
Storage — Bigtable, Cloud Storage, Firestore
Orchestration — Cloud Composer (Airflow), Workflows
Vertex AI — Model training, hyperparameter tuning, pipelines, model registry
Frameworks — TensorFlow, PyTorch, scikit\-learn MLOps — Model serving (Vertex AI Endpoints), monitoring, retraining pipelines Feature Store — Vertex AI Feature Store Search ML — Ranking, relevance tuning, embeddings (nice to have) AI \& LLM Gemini — Gemini API, Gemini Pro, multimodal usage Vertex AI
Generative AI — Model Garden, grounding, tuning RAG — Retrieval Augmented Generation, Vector Search (Vertex AI Vector Search) Agentic AI — Multi\-agent frameworks, tool use, autonomous workflows
Minimum Education:
The knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job.
Preferred Education:
No additional education
Minimum Years of Work Experience:
3
Preferred Years of Work Experience:
No additional years of experience
Minimum Leadership Experience:
None
Preferred Leadership Experience:
None
Certifications:
None
Competencies:
Global Perspective
Manages Ambiguity
Nimble Learning
Self\-Development
Collaborates
Cultivates Innovation
Situational Adaptability
Communicates Effectively
Drives Results
Interpersonal Savvy
Apply End Date: 07/14/2026
Salary Context
This $90K-$170K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At The Home Depot, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($130K) sits 41% below the category median. Disclosed range: $90K to $170K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
The Home Depot AI Hiring
The Home Depot has 4 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Atlanta, GA, US, Denver, CO, US. Compensation range: $170K - $190K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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