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About This Role
AI Sales Enablement Lead
STARK \| New York, NY (Hybrid)
Build the AI tools that make sales easier.
STARK is building toward becoming an AI\-native company.
We recently hired an AI Chief of Staff, and it has been a huge success. That role has focused primarily on operations, reporting, and workflow automation outside the sales organization. Now we are looking for a peer role dedicated specifically to sales.
This role is for someone who is AI\-native, highly practical, and excited to make a legacy sales organization dramatically easier to operate. The goal is simple: help salespeople spend less time on computer work and more time serving clients, and help sales managers spend less time chasing information and more time coaching, supporting, and driving performance.
This is not a theoretical AI role.
It is not a pure training role.
It is not a traditional sales ops job.
It is a build\-and\-enable role for someone who can learn how work actually happens, map the friction, design better workflows, build useful internal tools, and help salespeople and managers use AI every day.
About STARK
STARK is a luxury carpet and rug company with an 85\-year history and a strong reputation in the design community. We are a real company with real operational complexity, real data problems, real workflow friction, and real opportunity.
We are investing in AI because we believe the future of work is not just better software — it is better systems, better workflows, and better leverage for people.
We want to rebuild parts of our company to feel less like a traditional legacy business and more like a technology company that sells carpet.
Why This Role Exists
Our salespeople and sales managers work in an environment where:
- Systems are clunky
- Data lives in multiple places
- Activity tracking is inconsistent
- Too much time is spent on administrative work
- AI adoption is still early
- Best practices are not easy enough to follow
We want to fix that.
This role will focus specifically on making the salesperson and sales manager workflow easier, smarter, and more AI\-enabled.
What You’ll Do
Learn how the sales organization actually works
A major part of this job is discovery.
Before building the right tools, you’ll need to understand how work really happens. That means:
- Interviewing stakeholders across sales leadership, showroom management, and sales support
- Shadowing sales managers and salespeople
- Mapping workflows, handoffs, pain points, and administrative burden
- Identifying where AI, automation, or workflow redesign can create the most value
- Translating messy legacy ways of working into clearer tool and workflow requirements
We do not want someone who rushes to build the wrong thing.
We want someone who learns quickly, asks smart questions, and builds from real process understanding.
Build AI\-native sales tools and workflows
You will design and launch practical internal tools that improve the day\-to\-day work of salespeople and sales managers.
Initial focus areas include:
- A CRM\-light workflow layer for salespeople
- A sales manager control center
- Better customer and account search/navigation
- Easier visibility into emails, meetings, samples, quotes, and pipeline
- Tasks, reminders, and next\-best\-action support
- Email templates, sequences, and follow\-up workflows
- Smarter activity logging and meeting capture
- Internal sales tools that simplify workflow instead of adding more noise
Help salespeople use AI every day
We want AI to become normal inside the sales organization.
You will help drive that by:
- Training managers and salespeople on practical AI use cases
- Helping users get set up with tools and workflows
- Creating reusable prompts, Claude skills, and manager workflows
- Embedding AI into sales follow\-up, prep, analysis, and planning
- Making AI feel simple, useful, and relevant
Improve workflow consistency and raise the floor
Some top performers will always do things their own way. That is not the main target.
The bigger opportunity is helping average and developing salespeople operate with better structure, better follow\-up, and easier access to best practices.
This role should help:
- Reduce administrative burden
- Improve activity capture
- Make manager coaching easier
- Give salespeople an “army of agents” to support their work
- Raise average sales productivity by making the right behavior easier
Collaborate with the AI Chief of Staff
This role reports to the CEO and works as a peer to our AI Chief of Staff.
That role primarily focuses on workflow automation, reporting, and AI enablement outside the sales organization. This role focuses specifically on implementing AI inside the sales organization.
You will collaborate on:
- Shared data and reporting foundations
- Internal applications built on common infrastructure
- Intersections between sales workflow and broader operational workflow
- Identifying where sales needs overlap with automation opportunities elsewhere in the company
What Success Looks Like
In the first 90 days, success likely means:
- Interviewing stakeholders and mapping key sales workflows
- Identifying the highest\-friction opportunities in the salesperson and manager workflow
- Launching a functional v1 of a CRM\-light workflow system for pilot users
- Helping implement a sales manager control center
- Delivering a few practical workflow wins that make managers and salespeople feel the benefit immediately
- Beginning to build real excitement inside the sales organization around how AI can help them
At 6 months, success means:
- The pilot users are genuinely excited about the tools
- AI is becoming part of daily sales workflow
- Sales managers have better visibility and better prep
- Salespeople are doing less manual admin work
- The sales organization feels meaningfully easier to operate
What This Role Is Not
This role is not:
- A traditional sales manager role
- A people management role
- A pure trainer or facilitator role
- A deep data engineering or infrastructure role
- A back\-office ops automation role unrelated to sales workflow
- A generic executive assistant
There may be some administrative responsibilities related to the sales organization, such as goal tracking, meeting support, communication support, or follow\-up around sales leadership workflows, but only where those responsibilities improve sales operations and can be systematized or automated over time.
Who You Are
You might be a fit if you are:
- AI\-native and think AI\-first about workflow design
- A builder with an operator mindset
- Practical, not theoretical
- Energized by simplifying messy, clunky systems
- Strong at translating business pain into usable tools
- Humble, curious, and eager to learn process before forcing solutions
- Comfortable working directly with senior leaders
- Able to move quickly without overcomplicating things
- Excited by helping a legacy company become AI\-native
Skills and Experience
We care more about capability than pedigree, but strong candidates will likely bring some mix of:
- Demonstrated ability to build practical internal tools, automations, or lightweight apps
- Strong fluency with AI tools and workflows
- Ability to conduct stakeholder interviews and process mapping
- Good workflow design instincts and user empathy
- Ability to work with data using tools such as SQL, Power BI, Power Automate, data warehouses, or similar
- Comfort training and enabling non\-technical users
- Strong written and verbal communication
- Bias for simplicity, usability, and speed over overengineering
Helpful backgrounds might include:
- AI\-native operator with sales enablement instincts
- Sales ops or revenue ops person who became AI\-native
- Builder who understands CRM workflow and manager needs
- Early\-career, unusually high\-output operator with strong learning velocity
Compensation
This role is designed for a high\-upside builder/operator profile, and compensation will reflect that level.
Why Join Now
This is a chance to help define how a real sales organization evolves into an AI\-native one.
You won’t be writing a strategy deck about the future.
You’ll be building the future here, in a real company, with real users, real friction, and real stakes.
If that sounds energizing, we’d like to hear from you.
Benefits:
- Health Insurance
- Dental Insurance
- Vision Insurance
- Supplemental Benefits
- Great Paid Time Off Policy
- 401k match
COMPANY INFO
About Us:
With more than 80 years of industry expertise, STARK is the discerning designer’s go\-to source for luxurious custom carpet and rugs.
Our Mission:
To live our mission of helping create world class experiences, we believe our first responsibility is making design industry professionals the heroes in their projects because supporting businesses helps to preserve craftsmanship and enable unique and specially designed spaces to exist. In meeting their needs, we must continue to deliver peace of mind service while offering an expansive, diverse, and innovative product assortment that is accessible and convenient.
Our Heritage:
Established by Arthur Stark and his wife Nadia in 1938, the foundation of STARK was always to provide the very best products to our customers. With a rich history, third generation owners Chad Stark, as CEO, and Ashley Stark, as Creative Director, bring the STARK legacy into the new century by staying true to their founding principles. To seek out the best craftsman and methods of manufacturing for the most innovative and beautiful designs.
*STARK is a certified Great Place to Work® (June 2026– June 2027\) — and that comes straight from our people. 85% of our employees say STARK is a great place to work, 28 points above the typical U.S. company. See our full certified profile and what our team says about working here: greatplacetowork.com/certified\-company/7023725*
*Stark Carpet Corporation provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state and local laws.*
The pay range for this role is:
70,000 \- 80,000 USD per year(New York 780 Building)
Salary Context
This $70K-$80K 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 Stark Carpet, 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 ($75K) sits 66% below the category median. Disclosed range: $70K to $80K.
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.
Stark Carpet AI Hiring
Stark Carpet has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $80K - $80K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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