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Description:
This isn't a traditional entry\-level sales role. We’re building a new model for business development, one where AI accelerates research, personalization, and execution, while human judgment creates the strategy. We’re looking for someone who wants to help build the playbook, not just follow one. If you're naturally curious and enjoy figuring things out without being handed all the answers, keep reading. We're looking for someone who is excited about AI, eager to learn, and motivated to build a career.
OVERVIEW
Macedon Consulting sells tailored AI and process\-automation solutions to clients across healthcare, financial services, energy, and manufacturing. We sell complex services to demanding, heavily regulated buyers. This is the role that gets us in the room.
You find the right accounts, reach the right people, and book qualified meetings for our senior sellers. You will use AI to open conversations with people who have never heard of us and do not think they need another vendor. You will introduce organizations to new possibilities, build relationships with key decision\-makers, and create opportunities that drive growth for both our clients and Macedon.
Requirements: HOW YOU WILL USE AI
- Research an account in minutes, not hours. AI pulls the background, the signals, and the decision\-makers. You decide what matters and who is worth going after.
- Generate outreach with AI, then edit. Cut the fluff until it reads like it was written by a person who actually read the buyer's last earnings call.
- Run repeatable AI workflows for list building, enrichment, and meeting prep.
- Oversee an AI\-maintained CRM. AI logs the calls and updates the records. You audit the machine, fix the exceptions, and keep it clean enough that an Account Executive can act on it without calling you first.
WHAT YOU WILL DO
- Build and executive daily AI\-assisted outbound strategies across email, LinkedIn, and phone against target accounts from the CGO and marketing.
- Use AI to research accounts and find decision\-makers, priorities, and timing signals.
- Draft and personalize outreach with AI, then apply your own judgment before every send.
- Qualify inbound interest and book discovery meetings for Account Executives.
- Oversee the AI\-maintained CRM. Confirm what it logged is accurate, handle the exceptions, and keep records clean.
- Build and enrich target lists through AI\-assisted workflows.
- Learn fast how buyers in regulated industries think and why their legacy systems fail them.
- Test new tools and tactics, keep what works, and tell the CGO what you found.
THIS ROLE IS FOR YOU IF
- You get more energy from cracking a hard problem with no playbook than from following one.
- You would rather take a new tool apart and push it to its limit than wait for training.
- You treat a new AI model as raw material, not a novelty.
- You experiment with LLMs that make most people nervous, and you taught yourself to do it.
WHAT WE ARE LOOKING FOR
- We’re seeking an early\-career professional (0–12 months out of school) who is excited to build a foundation in business development/sales.
- The tools and technologies you use today will continue to evolve tomorrow, success comes from staying adaptable and continuously learning.
- You already run daily work through LLMs and other AI tools and have taught yourself to use technology in ways most people have not. School projects and side experiments count.
- You are a ruthless editor. You can take an AI draft and cut it into a short, sharp message a busy executive actually reads.
- You are curious, organized, and self\-directed. You work remotely without anyone looking over your shoulder.
- You can get up to speed quickly on CRM and sales\-engagement tools. Experience from school or an internship counts. You do not need to have used them before. You are a fast learner.
- Bachelor's degree or equivalent practical experience.
- A real interest in consulting, enterprise technology, and the industries we serve.
HOW SUCCESS IS MEASURED
- Qualified meetings booked with real decision\-makers at target accounts across the industries we serve, growing as you ramp over your first two quarters.
- Outbound volume and the quality of the conversations it starts.
- How fast and how well you turn AI research into outreach that gets replies.
- A CRM clean enough that an Account Executive can act on it without checking with you first.
COMPENSATION
The base salary for this role is $65,000 with 20% commission. This position may require up to 10% travel or in person meetings.
Macedon Technologies is not able to provide visa sponsorship for this position. Candidates must have ongoing authorization to work in the United States without employer sponsorship.
To learn more about our wonderful benefits, culture, and people, check us out at: About Us \| Macedon Technologies .
*Macedon Technologies is an equal employment opportunity employer and does not discriminate against applicants or employees* *on the basis of* *race, color, national origin, religion, sex, age, gender identity, disability, veteran status or sexual orientation. We are committed to ensuring that all aspects of employment – including recruitment, promotion, compensation, benefits and training – are based on equal employment opportunity principles. Macedon Technologies participates in E\-Verify.*
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 Macedon Technologies, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $200,000.
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.
Macedon Technologies AI Hiring
Macedon Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Reston, VA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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