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About This Role
About the role
Betco Sales Corp is seeking a Head of AI to lead the company's artificial intelligence strategy and put AI to work in every department. Betco is a 40-year, R2- and ISO-certified refurbished-electronics business in Moonachie, NJ with 150+ employees. We buy, refurbish, grade and resell phones and consumer electronics through carrier trade-in programs, liquidation lots, wholesale distribution, online marketplaces and live selling.
The ideal candidate combines a strong technical background with executive-level leadership: someone who can set the AI roadmap, build the team and infrastructure behind it, and still write code and ship tools themselves.
This is a hands-on builder role, not an advisory one. You will walk the warehouse, sit with sales, finance, purchasing, refurbishing and e-commerce, find where AI and software remove manual work or sharpen decisions, and then deliver the solution. You report directly to the CEO.
What you'll do
- Develop and execute Betco's AI strategy, aligned with business goals, with scalable and sustainable solutions.
- Audit every department (purchasing, receiving, refurbishing and grading, sales, e-commerce and live selling, finance, logistics, HR) and map where AI or automation saves time, money or errors.
- Oversee the design, development and deployment of AI solutions using machine learning, natural language processing, computer vision and emerging technologies.
- Build and ship internal software and AI tools: pricing and bid models for device auctions, listing and description generation, SKU and inventory matching, customer-service automation, document and email processing.
- Own company reporting: build dashboards and automated reports on sales, margin, inventory, yield and throughput so leadership sees accurate numbers daily, not monthly.
- Integrate AI into the systems we already run (our ERP and inventory systems, marketplace APIs, carrier portals, spreadsheets) rather than adding disconnected tools.
- Manage technical architecture planning for cloud infrastructure, databases and enterprise systems integration.
- Ensure data security, privacy compliance (including PCI) and ethical AI use; establish governance for risk management, disaster recovery and business continuity.
- Lead, mentor and manage a multidisciplinary team of engineers, data analysts and contractors as the function grows; run projects with agile methods and sound SDLC practices.
- Train managers and staff to use AI tools in their daily work and write the standards for how AI is used with company and customer data.
- Prioritize by return: measure the hours and dollars each project saves and report results to the CEO monthly.
- Stay current with advances in AI, cloud computing and DevOps, and foster a culture of innovation across the company.
What we're looking for
- Extensive experience in AI development, including at least 2 years shipping AI/LLM-based tools that real users depend on; proficiency with machine learning frameworks (Python, TensorFlow, PyTorch) and current AI APIs (OpenAI, Anthropic, Google, open-source models).
- Daily, practical fluency with AI tools: prompt design, retrieval, agents and automation platforms.
- Strong software development skills across front end and back end (Python, JavaScript/TypeScript, Node.js, React or similar) and comfort building full working applications, APIs and integrations end to end.
- Strong with numbers: SQL, spreadsheets, data modeling, ETL and data warehousing, and the ability to build reports and dashboards leadership can trust (Power BI, Looker, Metabase or similar).
- Strong knowledge of cloud platforms (AWS, Azure) and cloud infrastructure management, including databases (SQL Server, NoSQL), storage and cloud security.
- Familiarity with enterprise applications such as ERP, inventory and e-commerce systems and experience with API integration.
- Working understanding of networking and IT governance: VPNs, firewalls, encryption, PCI compliance, NIST standards, disaster recovery.
- Demonstrated leadership of cross-functional teams in agile environments; proven success in project and program management at an executive level.
- Proven record of walking into a non-technical department, understanding its workflow and delivering a tool people actually adopt.
- Strong communicator who can explain technical trade-offs to a CEO and train a warehouse lead with equal ease; adept at vendor management and strategic planning.
- Self-directed and fast: comfortable owning projects from idea to production with little oversight.
Nice to have
- Background in wholesale, e-commerce, reverse logistics, consumer electronics or refurbishing.
- Experience with computer vision (device grading, defect detection, barcode and screen reading).
- Experience with online marketplaces or live-selling platforms.
- Spanish proficiency.
- Experience building and leading a small technical team.
Details
- Location: on-site at 300 East Park St, Moonachie, NJ 07074. Hybrid considered for the right candidate.
- Reports to: CEO.
- Type: full-time.
Join us as our Head of AI to shape the future through innovative technology solutions that make a real impact!
Pay: $60,000.00 - $120,000.00 per year
Work Location: In person
Salary Context
This $60K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Betco Sales Corp, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($90K) sits 58% below the category median. Disclosed range: $60K to $120K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Betco Sales Corp AI Hiring
Betco Sales Corp has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Moonachie, NJ, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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