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About This Role
Position Summary
The Artificial Intelligence/Machine (AI/ML) Learning Engineer plays a vital role in designing, building, and implementing enterprise-grade Artificial Intelligence and Machine Learning solutions. This position focuses on leveraging the Microsoft and Azure stack to create and deploy intelligent automation, predictive analytics, and other AI-powered tools. This role will collaborate closely with business units to gather requirements and scope projects, and drive them to resolution, while also helping to establish and build out the company's AI Center of Excellence (COE).Position Description
Essential Duties and Responsibilities include the following. Other duties may be assigned.
- Design and implement end-to-end AI and Machine Learning solutions, platforms, and tools.
- Develop solutions focused on Intelligent Automation and Predictive Analytics.
- Build and deploy models predominantly within the Microsoft and Azure Stack (e.g., Azure ML, Cognitive Services, Azure Databricks).
- Leverage the Google Vertex AI Stack(e.g., BigQuery, VertexAI, Imagen, Veo, Gemini API and Agentic Platform) where the toolset meets the business need.
- Collaborate with business stakeholders to gather user requirements, define project scope, and translate needs into technical specifications.
- Assist in the development, documentation, and governance of the AI Center of Excellence (COE).
- Drive AI/ML projects from initial conception and design through to deployment, monitoring, and resolution.
- Design and conduct experiments and tests with a focus on model performance, scalability, and accuracy.
- Collaborate with data engineers, developers, and other IT personnel to design and integrate compatible solutions.
- Evaluate existing systems and provide advice on integrating new AI/ML capabilities.
- Stay current with the latest advancements in AI, machine learning, and the Azure ecosystem.
Qualifications
Education/Experience: Bachelor's degree (B. S.) or equivalent from four-year college in Computer Science, Data Science, Artificial Intelligence, or related field preferred. 3-5+ years of hands-on experience in data science, machine learning, or AI engineering. Demonstrable experience with the Microsoft and Azure AI stack (e.g., Azure ML, Azure Cognitive Services, Azure Databricks, Power BI). Equivalent combination of education and experience will be considered.
Computer Skills: To perform this job successfully, an individual must have the following core capabilities. Proficiency in programming languages such as Python or R. Deep experience with the Microsoft Azure AI stack: Azure Machine Learning, Cognitive Services, Azure Databricks, Azure Data Factory. Experience with ML frameworks and libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Strong understanding of data modeling, data warehousing, and predictive analytics. Knowledge of MLOps principles and tools for model deployment and management.
Certificates and Licenses: CCNA / CCNP preferred but not required.
Supervisory Responsibilities: This position will not have supervisory responsibilities.
Position Hiring Range
The hiring pay range provides a good faith estimate of the salary or hourly wage that Living Spaces expects to pay for the position upon hire. Pay will be determined by several factors, including, but not limited to: applicant's education, relevant work experience, knowledge, applicable and relevant skills and abilities, market demand, Company budget, as well as internal equity and alignment with geographic market data. Living Spaces reserves the right to modify this page at any time.
Compensation: $105,000.00 - $140,000.00 Overtime pay is available for eligible, non-exempt Team Members. Reimbursement for expenses as required by applicable law and Company policy. Retail, Guest Services and Distribution Center Team Members are eligible to receive team bonus based on meeting specific goals and KPI's.
Additional available benefits upon meeting eligibility requirements include:
- Medical (full-time only)
- Dental (full-time only)
- Vision (full-time only)
- 401(k) with Company match (full and part-time)
- Vacation (full-time only or as otherwise required by applicable law)
- Paid Sick Leave (full and part-time)
- Flex or Health Spending Account (for eligible full-time only)
- Employee Assistance Program (full and part-time)
- Holiday pay (full-time only)
- Life insurance (full-time only)
For more details, please visit our website at: Careers (livingspaces.com)
Equal Opportunity Employer
It is our policy to abide by all federal, state, and local laws prohibiting employment discrimination based solely on a person’s race, color, religious creed, sex, national origin, ancestry, citizenship status, pregnancy, physical disability, mental disability, age, military status, or status as a Vietnam-era or special disabled veteran, marital status, registered domestic partner or civil union status, gender (including sex stereotyping and gender identity or expression), medical condition (including but not limited to, cancer related or HIV/AIDS related), sexual orientation, or any other protected status except where a reasonable, bona fide occupational qualification exists.
E-Verify
Living Spaces participates in E-Verify. All newly-hired team members are queried through this electronic system established by the Department of Homeland Security (DHS) and the Social Security Administration (SSA) to verify their identity and employment eligibility.
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Salary Context
This $105K-$140K 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 Living Spaces, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($122K) sits 42% below the category median. Disclosed range: $105K to $140K.
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.
Living Spaces AI Hiring
Living Spaces has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in La Mirada, CA, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 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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