Interested in this AI/ML Engineer role at Business Development Resources?
Apply Now →Skills & Technologies
About This Role
What We Do:
Business Development Resources, Inc. (BDR) provides training and coaching services to the contracting industry. At BDR, our goal is simple: empower our clients with our industry experience and information to give them the knowledge they need to drive profit and growth in their business.
Our Vision:
We will be the model of excellence, challenging traditional boundaries, seizing opportunities, and being the renowned provider of innovative products and services.
Our Mission:
Through teamwork, we will increase the profitability and efficiency of our clients to prime while maintaining an environment that fosters unequaled team member growth and success.
Prime means the most flourishing stage or state, reaching perfection.
Our Values:
Absolute honesty and integrity
Uphold the highest level of confidentiality and trust
Empowered, passionate, heartfelt, caring, and supportive of our clients and teams
Teamwork is the source of our strength
Change is essential, and we will always embrace it
We encourage our family to grow, contribute, and accomplish
Leading the industries we serve through innovation and creativity
Consistent, actionable knowledge transfer to those we serve
Owning our results and being accountable to ourselves, our team, and our clients
BDR is seeking a high\-level, consultative sales professional to join our team as a Distributor Training Sales Representative. This role is responsible for driving new client acquisition at a national level within the HVAC, plumbing, and home services industries, specifically targeting manufacturers and distributors. You will build strategic relationships, identify training needs, develop customized training journeys, and present solutions across BDR's full suite of business training and consulting services.
What You'll Do:
Identify, prospect, and acquire new distributor and manufacturer clients across the U.S. while building long\-term relationships that support a recurring sales cycle, develop account plans and tailored training strategies based on client needs, conduct discovery conversations and present value\-based solutions with a strong ROI focus, manage a consistent and active sales pipeline that meets or exceeds growth targets, collaborate with marketing and leadership to drive lead generation and improve go\-to\-market strategies, maintain accurate and detailed activity within Dynamics CRM, represent BDR at trade shows, industry events, and presentations to strengthen partnerships and brand visibility, stay current on industry trends, emerging technologies, and training solutions, and contribute to the development of sales tools, presentations, and promotional materials.
Work Schedule and Location:
Monday through Friday, typically 7:00 a.m. to 4:00 p.m. with flexibility as needed for business demands, remote or hybrid work environment with travel required for client meetings, events, and industry functions.
Compensation:
Competitive, experience\-based compensation aligned with senior\-level sales expertise and performance expectations, with strong earning potential tied to results and growth.
BenefitsPerks:
Full benefits package including medical, supplemental coverage, and employer\-supported plans
401k with employer match
Paid time off and paid holidays
Employee Assistance Program and life insurance options
Opportunity to work with a highly tenured, growth\-focused team
What You Bring:
Minimum 7 years of experience in new client acquisition sales with at least 10 years of overall sales experience
Proven success in B2B consultative or advisory services sales
Strong knowledge of the HVAC, plumbing, or home services industry, with distributor experience preferred
Demonstrated ability to build and manage a high\-performing sales pipeline and exceed targets
Experience delivering needs\-based presentations and solution selling
Proficiency with CRM systems such as Microsoft Dynamics and standard business tools
Strong communication, organization, and time management skills
Bachelor's degree preferred
Why Work for Us?:
Named one of Washington's Best Companies to Work For
Average employee tenure of over 7 years
A team that is passionate about client success
Supportive leadership with clear expectations and communication
Strong career growth opportunities with ongoing training and development
Established, profitable company with a long\-standing reputation in the industry
Professional, collaborative, and engaging work environment
What are you waiting for? Apply today and be part of a company that is redefining success in the contracting industry.
Salary Context
This $50K-$70K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Business Development Resources, 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. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($60K) sits 64% below the category median. Disclosed range: $50K to $70K.
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.
Business Development Resources AI Hiring
Business Development Resources has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Cleveland, OH, US, Virginia Beach, VA, US. Compensation range: $70K - $70K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.