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About This Role
Company Description
QAD is building a world\-class SaaS company, and we are growing. We are looking for talented individuals who want to join us on our mission to help solve relevant real\-world problems in manufacturing and the supply chain.
At QAD, we don't just build software; we build the future of manufacturing. Operating with the speed, agility, and precision of a Formula One team, our Adaptive ERP suite—encompassing world\-class ERP, Champion AI, Enterprise Quality Management Systems (eQMS), and Supplier Relationship Management (SRM)—empowers global manufacturers to turn supply chain turbulence into a competitive advantage. We are fast\-paced, innovative, and deeply passionate about delivering transformational solutions to our clients.
Job Description
We’re looking for an innovative Revenue Marketing Manager for our Supply Chain business unit who thrives in a fast\-paced SaaS environment and knows how to translate strategy into measurable, repeatable pipeline impact. An ideal candidate is a creative self\-starter who is not afraid to be scrappy, hands\-on, with the ability to build long\-term campaign programs from scratch with revenue accountability. This role owns demand generation, field marketing, and Account\-Based GTM (ABX/ABM) execution across a key product line in a multi\-product Supply Chain business unit. You’ll work closely with Sales and BDRs to accelerate engagement among ICP accounts, increase meeting creation, and influence pipeline and revenue outcomes.
If you’re energized by moving fast, building out\-of\-the\-box creative campaign programs, experimenting, measuring everything, and partnering tightly with Sales, this role will fit you well.
*What you will do:*
Account\-Based GTM \& Demand Generation
- Build and execute long\-term, strategic and creative integrated demand programs and local events that raise ICP engagement, meeting creation, and opportunity progression.
- Run full\-funnel ABM/ABX multi\-product Supply Chain GTM motions across targeted account segments including paid media, content, email, events, and outbound support.
- Develop activation plans for Tier 1/2/3 accounts with clear goals by stage (engagement meetings SAO pipeline).
- Partner with Sales and BDRs on account selection, signal interpretation, and meeting readiness criteria.
Pipeline \& Revenue Impact
- Own pipeline creation targets and influence goals aligned to quarterly and annual revenue objectives.
- Track and optimize conversion pathways across channels: digital advertising, paid/organic content, events, campaigns, and outbound orchestration.
Campaign Execution
- Create multi\-channel Supply Chain campaigns supporting product lines, use cases, and vertical segments.
- Manage budgets across ABM, paid media, content syndication, and events with a focus on efficiency and ROI.
- Partner with Product team, SMEs, and Sr Content Strategist to translate messaging into campaigns that engage, resonate, and convert ICPs into pipeline.
Technology, Data \& Measurement
- Work with Marketing Operations group to leverage ABM and marketing automation platforms, CRM, and analytics tools to track engagement progression and campaign performance.
Maintain dashboards that show account movement, influence, pipeline contribution, and velocity.
Identify insights and turn them into actionable program adjustments.
*
Cross\-Functional Collaboration
- Work closely with Sales, BDR, Customer Success, and Product teams to maintain a unified revenue approach.
- Support Sales Accepted Meeting (SAM) workflows, opportunity quality alignment, and post\-meeting progression.
- Participate in regular GTM planning, campaign prioritization, and lead\-management processes.
Qualifications
- 2\+ years of experience in B2B SaaS demand generation or revenue marketing roles.
- Self\-starter with strong understanding of SaaS funnels, meeting conversion benchmarks, and pipeline math.
- Experience with ABM platforms (N.Rich, 6Sense, DemandBase), and marketing automation (HubSpot/Marketo), CRM (Salesforce).
- Comfortable operating in a fast\-paced, iterative environment where speed and adaptability matter.
- Strong analytical skills with the ability to translate data into insights and recommendations.
- Excellent cross\-functional communication and stakeholder management.
- Preferred experience executing Account\-Based GTM/ABM programs with measurable pipeline impact.
- Preferred experience in the supply chain, logistics, manufacturing, or ERP ecosystems
Success Looks Like
- Increased qualified meetings and pipeline aligned to ICP priorities.
- Strong account engagement progression reflected in multi\-channel activity.
- Improved efficiency across paid channels and campaigns to back into pipeline goals.
- Clear, actionable reporting that Sales and Leadership rely on for decision\-making.
- Collaborative, proactive partnership with Sales and BDRs with effective results\-driven communication
Additional Information
- Your health and well being are important to us at QAD. We provide programs that help you strike a healthy work\-life balance.
- Opportunity to join a growing business, launching into its next phase of expansion and transformation.
- Collaborative culture of smart and hard\-working people who support one another to get the job done.
- An atmosphere of growth and opportunity, where idea\-sharing is always prioritized over level or hierarchy.
- Compensation packages based on experience and desired skill set.
COMPENSATION PACKAGE:
- Base pay range: $100,000 \- $114,000 USD Annual (12 Months).
- Placement within our pay range will vary based on knowledge, skills, experience, and market location variations as well as internal peer equity.
- This position is eligible for commission incentives
- U.S. benefits package includes medical, dental and ,vision coverage, a 401(k) plan with company match, short\-term and long\-term disability coverage, life insurance, paid time off, parental leave, flexible spending accounts and employee assistance program.
About QAD:
QAD \| Redzone is redefining manufacturing and supply chains through its intelligent, adaptive platform that connects people, processes, and data into a single System of Action. With three core pillars — Redzone (frontline empowerment), Adaptive Applications (the intelligent backbone), and Champion AI (Agentic AI for manufacturing) — QAD \| Redzone helps manufacturers operate with Champion Pace, achieving measurable productivity, resilience, and growth in just 90 days.
QAD is committed to ensuring that every employee feels they work in an environment that values their contributions, respects their unique perspectives and provides opportunities for growth regardless of background. QAD’s DEI program is driving higher levels of diversity, equity and inclusion so that employees can bring their whole self to work.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.
\#LI\-Remote
Salary Context
This $100K-$114K range is above 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 QAD, Inc., 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 ($107K) sits 36% below the category median. Disclosed range: $100K to $114K.
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.
QAD, Inc. AI Hiring
QAD, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $114K - $114K.
Location Context
AI roles in Chicago pay a median of $202,350 across 310 tracked positions. That's 10% above the national 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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