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About This Role
Location: Remote
We inspire purpose\-filled living that brings beauty and quality to the modern home. Together, we achieve. Associates across our business drive results, innovate, and inspire. Drawn together by our shared values and passion for our customers and our brands, we deliver home furnishings that are expertly designed, responsibly sourced, and bring timeless style and function to people’s homes. From the day we opened our first store in Chicago in 1962 to the digital innovations that engage millions of customers today, our iconic brand is over 60 years in the making—and our story is still unfolding.
We’re here for it. We think *you should be too.*
We are seeking a highly experienced and passionate Lead AI/ML Engineer to guide our core data science initiatives. With 8\+ years of dedicated experience in the AI/ML domain, you won't just be optimizing algorithms; you'll be setting the technical vision for our next generation of intelligent products. This role offers the unique opportunity to bridge the gap between cutting\-edge research and real\-world impact, overseeing the entire ML lifecycle—from ideation and experimentation to
- This position is fully remote
- This role is an Individual Contributor position
A day in the life as a Lead AI/ML Engineer...
- Provide strong technical leadership and guidance to the machine learning engineering team, setting the technical vision and ensuring alignment with product goals
- Lead the design, implementation and rigorous evaluation of highly scalable and performant ML models (e.g., deep learning, NLP, computer vision) to solve complex business problems
- Oversee and drive the full machine learning lifecycle, including data ingestion, model training, validation, deployment, and monitoring in production environments
- Actively participate in and champion team ceremonies contributing to the successful delivery of sprint goals and continuous process improvement
- Collaborate effectively with product managers, data scientists and other stakeholders to define requirements, author user and technical stories, and translate business needs into technical specifications
- Champion and implement best practices for MLOps (e.g., CI/CD, feature stores, model versioning) to ensure reproducible and reliable deployments
- Set coding standards, lead code reviews, and ensure best practices in source control management
- Mentor and guide junior and mid\-level engineers, fostering their technical growth, providing constructive feedback, and promoting a collaborative team environment
- Establish and champion high standards for knowledge management within the team, ensuring clear, comprehensive, and easily accessible documentation for all developed features and solutions, significantly enhancing team efficiency and codebase maintainability
- Stay up\-to\-date with the latest technologies and trends, proactively identifying opportunities for improvement and innovation within the engineering processes and technology stack
- Identify and mitigate technical risks, ensuring the timely and successful delivery of features and solutions
What you'll bring to the table...
- Deep expertise with Python and standard ML/scientific computing libraries (e.g., TensorFlow, PyTorch, scikit\-learn, Pandas)
- Deep theoretical and practical understanding of various machine learning algorithms, including modern deep learning architectures and methodologies
- Demonstrated expertise with MLOps tools and cloud\-native services (e.g., Kubeflow, MLflow, Docker, Kubernetes)
- Strong experience designing and deploying scalable ML solutions on major cloud platforms (AWS, Azure, or GCP)
- Solid understanding of data warehousing, ETL processes, and familiarity with distributed computing frameworks (e.g., Spark)
- Ability to design, articulate, and implement complex, highly available, and fault\-tolerant AI production systems
- Proven ability to set coding standards, lead code reviews, and manage software development lifecycles
- Exceptional problem\-solving, analytical, and strategic thinking skills
- Strong technical leadership skills with the ability to set technical vision and guide a team
- Deep understanding of agile software development methodologies and the software development lifecycle
We'd love to hear from you if you have...
- 8\+ years of progressive experience in machine learning engineering
- Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related field
- Demonstrated experience successfully taking multiple ML models from research/prototype phase into large\-scale production environments
- Prior experience working with big data technologies and designing robust data pipelines for ML training
Starting Rate: $130,000\.00 \- $160,000\.00
Pay ranges will be adjusted upward as needed to comply with applicable state and local law. In addition to your salary, based on your role, associates may be eligible for other compensation including bonuses, sales incentives, and long term incentives.
Job ID R24765 Date posted 06/09/2026 Position Type Full Time
Our commitment to our associates is of the utmost importance. One of the reasons the company attracts such a diverse group of associates is that we offer a full menu of benefits that are relevant to their lives, both on and off the job. We are proud to offer a comprehensive compensation and benefits package to support eligible part time and full time associates and their families, including:
- Medical/Dental/Vision
- Life insurance and Disability
- Retirement and 401(k) match
- Paid time off, wellness time and volunteer time
- Merchandise discount and EAP resources
- Tuition Reimbursement
Many of these benefits begin on day one, and extend to eligible dependents. To learn more about available benefits please click https://jobs.crateandbarrel.com/benefits
Euromarket Designs, Inc., which does business as Crate \& Barrel, Crate \& Kids, CB2 and Hudson Grace, will be referred to as “the Company”. The Company is deeply committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation for any part of the application process, or in order to perform the essential functions of a position, please contact the location you are applying to here and ask to speak with a manager regarding the nature of your request.
The Company is an equal opportunity employer; applicants are considered for all positions without regard to race, color, religious creed, sex, national origin, citizenship status, age, physical or mental disability, sexual orientation, gender identity, marital, parental, veteran or military status, unfavorable military discharge, or any other status protected by applicable federal, state or local law.
The Company participates in E\-Verify and will provide the federal government with your Form I\-9 information to confirm that you are authorized to work in the US.
State / City Compliance: The Company will consider for employment qualified applicants with criminal history, including arrest and conviction records, in accordance with the Los Angeles Fair Chance Initiative for Hiring and the San Francisco Fair Chance Ordinance.
Job Applicant Privacy: For details about how the Company collects and uses your personal information, please see our Job Applicant Privacy \& Communications Notice.
Questions? Please reach out to [email protected]
Salary Context
This $130K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2064 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 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Crate and Barrel, 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 $180,000 based on 12,398 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($145K) sits 19% below the category median. Disclosed range: $130K to $160K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Crate and Barrel AI Hiring
Crate and Barrel has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,883 positions. About 15% 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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $200,000. Top-quartile roles start at $253,000, 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 Engineering Manager roles lead at $290,000 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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