Interested in this AI/ML Engineer role at Resident Home Corporation?
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About This Role
THE BIGGEST NAME IN HOME. THE BOLDEST TEAM IN E\-COMMERCE.
Ashley Digital is the e\-commerce engine behind Ashley Furniture Industries — one of the most recognized home brands in the world. As the world's largest manufacturer of home furnishings and the largest furniture store brand in North America, Ashley is in a category unto itself. Our team sits at the intersection of world\-class retail and digital innovation, driving the e\-commerce experience for millions of customers across every platform and touchpoint. If you’ve shopped online for a mattress in the last decade, there’s a good chance you’ve already experienced our award\-winning Nectar Sleep and DreamCloud. We're a fast\-moving, highly collaborative team operating at a transformational moment, and we're charged with shaping how people discover, explore, and buy home furnishings. Our expertise spans data science, performance marketing, product, and creative, and our work reaches millions of customers every day. It's a rare combination: the energy of a tech company, the stability of an industry leader, and the opportunity to build something truly significant. If you're energized by transformation, collaboration, and meaningful impact at scale, Ashley Digital is where ambition meets opportunity.
To be considered for this remote opportunity, you must reside and be authorized to work within the United States.
About the Role:
Great AI conversations don't happen by accident, they're designed, measured, and continuously improved. As our AI Conversation \& Knowledge Manager, you'll own the performance of our customer\-facing AI experiences across Ashley Digital's brands. You'll combine customer insights, conversation design, analytics, and knowledge management to ensure every interaction helps customers achieve the right outcome quickly while driving measurable business results.
This role is equal parts strategist, analyst, writer, and systems thinker. You'll define how customers interact with AI, establish success metrics, analyze performance, identify optimization opportunities, and continuously improve conversational experiences through experimentation and iteration.
You'll partner closely with Product, Customer Support, Operations, CRM, QA, Legal, Brand, and AI Enablement teams to ensure our AI experiences remain accurate, conversational, on\-brand, and continuously optimized using customer sentiment, behavioral insights, and business performance metrics.
Success in this role isn't measured by the number of knowledge articles but through improvements in customer satisfaction, resolution quality, conversion, retention, operational efficiency, and the overall customer experience.
What You’ll Be Doing:
- Own the end\-to\-end performance of Ashley Digital's AI customer experience, defining customer intents, desired outcomes, success metrics, and conversation strategy across all AI workflows.
- Design, test, and continuously improve conversational experiences through prompts, response logic, routing, escalation strategies, and system instructions, ensuring every interaction feels natural, helpful, and true to the Ashley Digital brand.
- Balance automation with customer experience by determining when AI should resolve customer needs independently and when human support delivers the best outcome.
- Monitor customer sentiment, conversation quality, and behavioral analytics to continuously improve tone, clarity, empathy, resolution quality, and overall customer experience.
- Analyze AI performance using KPIs such as CSAT, One\-Touch Resolution, Total Resolution Time, containment, escalation, retention, conversion, and AOV to identify optimization opportunities and prioritize the roadmap.
- Build and maintain a continuous improvement framework by leveraging customer sentiment, QA findings, conversation analytics, operational data, and experimentation to optimize AI performance and customer outcomes.
- Conduct recurring conversation reviews to identify friction points, underperforming workflows, emerging customer trends, and knowledge gaps, translating insights into actionable improvements.
- Partner closely with Customer Support, Product, Engineering, QA, Operations, CRM, Legal, and Brand teams to align AI experiences with evolving customer needs, business priorities, and operational processes.
- Own and maintain the enterprise knowledge base, ensuring it serves as the trusted source of truth for both AI and human support.
- Establish scalable knowledge management practices, including taxonomy, metadata, governance, content lifecycle management, templates, and authoring standards that keep information accurate, AI\-ready, and easy to maintain.
- Continuously improve the knowledge base using conversation insights, customer feedback, and operational changes to ensure content remains current, relevant, and effective.
- Communicate performance trends, business impact, and strategic recommendations to stakeholders through clear reporting, dashboards, and data\-driven insights.
The responsibilities described above are not exhaustive. You may be asked, from time to time and as reasonably required by business needs, to perform other duties consistent with your skills and role, including work for or on behalf of our related entities. This job description does not constitute a contract of employment and may be modified at any time, subject to applicable law.
What Success Looks Like:
- Improve customer experience by increasing customer sentiment, CSAT, and One\-Touch Resolution while reducing Total Resolution Time.
- Optimize AI performance by increasing successful AI containment and reducing unnecessary escalations caused by knowledge or workflow gaps.
- Drive business impact by improving customer conversion, Average Order Value (AOV), and customer retention where AI supports the customer journey.
- Establish a measurable continuous improvement program that uses analytics, experimentation, customer feedback, and operational insights to identify issues, prioritize opportunities, and drive ongoing optimization.
- Deliver consistent, conversational, on\-brand AI experiences across every customer touchpoint.
Skills \& Qualifications:
- 5\+ Experience in LLM prompting, Conversation Design, UX Writing, Linguistics, or related CX operations
- Deep understanding of conversation AI workflows and data integrations.
- Strong writing and communication skills with exceptional attention to detail
- Experience analyzing customer conversations and identifying operational trends
- Strong problem\-solving and cross\-functional collaboration skills
- Experience with Intercom Fin or similar conversational AI platforms.
- Experience with BI and analytics platforms such as Looker, Tableau, Sigma, or similar reporting tools.
- Familiarity with experimentation frameworks
- Experience managing enterprise knowledge bases, preferably in Notion.
- Expertise with prompt engineering and Large Language Model (LLM) best practices.
What We Offer (subject to eligibility requirements):
- Remote\-first workplace (since 2016!)
- Competitive Salary
- Health, Vision \& Dental Insurance
- HSA company contributions
- 401K with company match component
- Take what you need Paid Time Off
- Wellness benefits
- WFH office and cell phone/internet stipend
- A FREE MATTRESS plus an awesome Friends and Family discount!
If you reside in a state or location where pay transparency laws or regulations have been adopted please read the following: The salary for this position is $90,000 \- $110,000\. We carefully consider a wide range of compensation factors, including your background, skills, qualifications, experience, geographic location and other non\-discriminatory factors. These considerations can cause your compensation to vary. \[Additionally, this role might be eligible for discretionary bonuses or commission payments]. For more information regarding the pay range applicable for this position, please contact us at [email protected]
Ashley Digital is a privately\-held company headquartered in Tampa, FL with offices in New York City, London and Tel Aviv. Learn more at: https://www.residenthome.com
Ashley Digital is committed to a policy of equal employment opportunity, and will not discriminate against an applicant or employee on the basis of race, color, religion, creed, national origin, ancestry, sex, gender, age, physical or mental disability, veteran or military status, genetic information, sexual orientation, gender identity, gender expression, marital status, or any other legally recognized protected basis under federal, state, or local law.
Ashley Digital is proud to be a remote\-first company and maintains policies to support our unique flexible work location culture. However, there are a few important parameters to our work\-from\-home\-culture: While we currently employ Residents in over 30 US states and 3 countries, if you are hired, you may be restricted to working from the state or country in which you currently reside, unless the state or country to which you plan to relocate is one in which we already operate and no other restrictions apply to the role. As with anything, we encourage an open dialogue about your current location and potential desired relocation during the interview process and upon hire, if applicable, and the extent any other restrictions apply to a particular role. We reserve the right to deny relocation requests post\-hire for any reason.
\#LI\-REMOTE
Ashley Digital participates in E\-Verify.
Salary Context
This $90K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Resident Home Corporation, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $90K to $110K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Resident Home Corporation AI Hiring
Resident Home Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $110K - $110K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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