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About This Role
Position Overview:
The Lead Retail Associate (“Shift Lead”) provides an exceptional experience for all customers and fellow team members. In this role, the Shift Lead will support Store leadership in day\-to\-day operations and lead by example as a positive, customer\-focused retail associate. The Shift Lead may hold a state designation as the manager on duty, key holder, or agent in charge, and as such, will be responsible for coaching and training employees and resolving customer issues as needed. The Shift Lead will work closely and collaboratively with the store leadership team to drive results in a fast\-paced environment while supporting the Company’s core values and culture.
Major Areas of Responsibility include:
- Support store leadership in achieving sales goals and business growth.
- Lead by example and promote a service\-minded retail environment.
- Demonstrate exceptional cannabis product knowledge and provide guidance to customers to ensure a positive experience.
- Open/close the store as needed and assign tasks as needed throughout the day to meet business demands.
- Perform and train the retail team on all retail tasks and responsibilities including, but not limited to, customer intake, sales, inventory, and POS transactions.
- Provide coaching in the moment to employees and escalate employee concerns or issues to store leadership in a timely manner.
- Ensure operational excellence by following SOPs, compliance regulations, and established internal processes; report any compliance issues, concerns, or violations to store leadership in a timely manner.
- In collaboration with store leadership, support all aspects of inventory management including but not limited to: ensuring inventory movement is properly documented and supporting regular audits and month/quarter/year\-end inventory processes
- Identify opportunities for improvement and share findings with store leadership.
Minimum Qualifications (Skills, Knowledge \& Abilities):
- All applicants must be at least 21 years of age
- 3\+ years of retail or customer service experience
- Experience with cash handling and Point\-of\-Sales systems required
- Exceptional customer service and problem\-solving skills
- Strong collaboration skills and the ability to contribute to a positive work environment
- Ability to coach and influence others
- Flexibility to work nights, weekends, and holidays as needed
Travel %: 0
FLSA status: Non\-Exempt
Additional Abilities Required:
This role is performed in a traditional retail setting which requires the ability to have a high stress tolerance and be flexible and adaptable. While performing the duties of this job, the employee is required to stand, walk, or sit for extended periods of time, use hands to perform manual tasks, use computer equipment such as POS and email, and lift or move up to 50 pounds (or more with assistance). Must be able to speak and communicate verbally with co\-workers, customers, vendors, etc. The noise level in the work environment is usually moderate.
Note: *Nothing in this job description restricts the company’s right to assign or reassign duties and responsibilities to this position at any time. Reasonable accommodations may be made in appropriate circumstances to enable individuals to perform the essential functions of the position.*
About The Cannabist Company (DBA The Green Solution \& Medicine Man):
The Cannabist Company, formerly known as Columbia Care, is one of the most experienced cultivators, manufacturers and providers of cannabis products and related services, with licenses in 12 U.S. jurisdictions. The Company operates 77 facilities including 61 dispensaries and 16 cultivation and manufacturing facilities, including those under development. Columbia Care, now The Cannabist Company, is one of the original multi\-state providers of cannabis in the U.S. and now delivers industry\-leading products and services to both the medical and adult\-use markets. In 2021, the Company launched Cannabist, its retail brand, creating a national dispensary network that leverages proprietary technology platforms. The company offers products spanning flower, edibles, oils and tablets, and manufactures popular brands including Seed \& Strain, Triple Seven, Hedy, gLeaf, Classix, Press, and Amber. For more information, please visit www.cannabistcompany.com.
Recognized for its comprehensive benefits, ongoing training opportunities and commitment to diversity and equity inclusion both internally and with external vendors, The Cannabist Company has earned a spot on mg Magazine’s America’s Top Cannabis Industry Employers list.
The Cannabist Company provides full\-time employees with an excellent benefits and compensation package including but not limited to competitive wages, paid holidays, vacation and sick time, 401K with company match, multiple health plan choices, dental, vision, pet and life insurances, 100% paid short\-term disability, optional long\-term disability insurance and more!
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At The Cannabist Company, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
The Cannabist Company AI Hiring
The Cannabist Company has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Denver, CO, US, Dayton, OH, US, Rehoboth Beach, DE, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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