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About This Role
Job Summary:
FreshPoint is seeking an experienced and results-oriented Vice President of Retail Sales to lead and oversee retail sales and merchandising activities that directly impact our valued customers. This strategic role combines a customer-centric approach with leadership in sales operations, ensuring the delivery of innovative solutions and top-quality produce to our clients. The VP of Retail Sales will develop strategies that protect, grow, and diversify customer relationships, while driving the company’s mission and success in the fresh produce industry.
This position is responsible for executing the center led strategies, ensuring top talent in the key positions, and leveraging selling resources to grow market share and enable a consultative team-based selling organization. This leader will coach, guide, and counsel sales team members to meet their individual sales and profit plans.
Duties and Responsibilities:
Retail Sales Leadership
- Oversee retail sales and merchandising activities to ensure alignment with customer needs and company goals.
- Responsible for seeking new retail and organics sales, monitoring, responding to, and initiating action for all corporate communication regarding CMU (corporate multi-unit) accounts and contracts
- Assess customer requirements and recommend suitable products, services, or innovative solutions.
- Directly engages with Key Functional Leaders for feedback, collaboration, issue resolution, and problem-solving
Strategic Sales Management
- Develop and deliver impactful sales bids, proposals, and presentations to secure new business opportunities.
- Responsible for the identification of new retail and organics national sales opportunities
- Create and implement both short-term and long-term strategic plans to enhance customer relationships and expand market presence.
Procurement & Market Expertise
- Maintain a strong knowledge of produce markets, including industry trends, pricing structures, and supplier relationships.
- Lead efforts in procuring high-quality fresh produce by negotiating favorable supplier contracts.
- Manage freight logistics to ensure cost-efficient and timely delivery of goods.
Team Development & Collaboration:
- Build, mentor, and lead a high-performing retail sales team.
- Foster collaboration across departments to ensure seamless service delivery and operational efficiency.
Performance Metrics & Reporting:
- Analyze sales performance data and provide regular reporting to the executive team.
- Continuously identify areas for improvement and implement solutions to drive business growth
Education Required:
Bachelor’s degree in business, marketing, or related studies
Education Preferred:
2–4-year degree in business or culinary preferred
Experience Required:
7-10 Years with extensive experience in sales, merchandising, and at least 5 years of management experience.
Technical Skills and Abilities:
- Strong collaboration and influence skills, and the ability to lead, listen, and influence at the senior-most levels of the organization.
- Leadership proficiency in Retail Sales, Revenue Management, Merchandising and execution.
- Proven experience in developing comprehensive deployment plans, relevant go to market strategies and execution plans.
- Demonstrates well-developed influencing skills with the ability to easily connect credibly with field leadership.
- Possesses a deep understanding and demonstrated capability for strategy, processes, capabilities, enabling technologies, and measurement.
- Exceptional communication skills and the ability to communicate appropriately at all levels of the organization; this includes written and verbal communications as well as visualizations.
- Able to drive consensus among key stakeholders with diverse needs and interests.
- Thinks and acts proactively rather than reactively and directs resources and stakeholders accordingly.
- Strong performance management capabilities with direct reports
- Solid analytical problem-solving skills, including familiarity with analyzing reports & deriving insights from data
- Strong financial acumen and ability to properly plan and execute business plans
- Ability to learn the use of proprietary CRM tools for planning and forecasting sales growth
- Business and restaurant operations acumen to manage sophisticated customers
- Demonstrated experience with building trust with a prospective customer and securing new business
- Demonstrated skills in consultative selling, networking, and negotiations
- Reports to work promptly and regularly; works well with others; demonstrates the ability to consistently meet deadlines
Language Requirements:
Fluency in English is required
Physical Demands:
Reasonable accommodations will be made to enable individuals with disabilities to perform the essential functions of this job.
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.
Travel Requirements:
Ability to travel up to 25% in the Region
Work Environment:
Must be able to utilize office equipment such as desktop/notebook computers, copiers, printers, scanners, telephones, and calculators.
The noise level in the work environment is usually moderate.
Must be able to work in various indoor and outdoor climates and driving conditions.
Salary Context
This $129K-$215K range is above the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Coastal, 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 $154,000 based on 8,743 positions with disclosed compensation. This role's midpoint ($172K) sits 12% above the category median. Disclosed range: $129K to $215K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Coastal AI Hiring
Coastal has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $215K - $215K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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