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About This Role
LeadVenture is looking for a Senior Manager, Growth Operations \& AI to be the hands\-on execution partner behind our growth marketing systems — HubSpot, Clay, and the workflows that connect Marketing to Sales, BDR, Revenue Operations, and Systems.
You will report to the Director of Growth Marketing and work in close partnership with her on strategy, while owning execution: building the workflows, automations, and AI\-enabled processes that make our campaigns faster, smarter, and easier to scale across LeadVenture's portfolio of brands.
This role is built for someone who wants to be deep in the tools and the data — someone who gets energy from making systems work better and finding practical ways to apply AI to real marketing problems. It's also not an order\-taking role: you'll partner directly with the Director of Growth Marketing on strategy, bringing ideas and pushing back when something's not working.
What You'll Do
Be the AI Lead for Growth Marketing
- Identify and build practical AI applications across segmentation, personalization, research, campaign development, and workflow automation
- Own our use of Clay for research, enrichment, audience development, and personalized campaign activation
- Test new AI tools and workflows, measure what actually moves the needle, and scale what works
Own HubSpot Post\-Migration
- Maintain, extend, and optimize HubSpot following our current implementation (in partnership with our implementation partner)
- Build and refine lifecycle workflows, lead scoring, routing, nurture programs, and reporting as our needs evolve
- Monitor data flows and platform health, catch breakdowns early, and fix them
Connect Marketing, BDR, Sales, and Systems
- Serve as Marketing's primary day\-to\-day partner to the Systems team on Salesforce, and to Sales, Revenue Operations, and BDR on Apollo — targeting, outbound workflows, and list quality
- Own how leads and data move between HubSpot, Salesforce, Clay, and Apollo — from capture to BDR follow\-up, fast and with full context
- Find and fix the operational gaps that slow campaigns down or lose leads between teams
Improve Data and Funnel Operations
- Maintain standards for data quality, enrichment, segmentation, and governance
- Build and refine lead scoring using fit, engagement, intent, and Sales feedback
- Improve visibility into lead progression, pipeline contribution, and conversion
- Create feedback loops between Marketing, Sales, BDR, and Systems
Think Strategically, Then Execute
- Notice what's not working and propose fixes — this is where your point of view matters most
- Partner with the Director of Growth Marketing to turn campaign and business priorities into technical and workflow requirements, shaping the "how" and often influencing the "what"
- Build standards and light documentation so processes hold up as we scale across brands
What Success Looks Like
- Within 90 days, you understand our tech stack, have built trust with Sales/BDR/Systems, and are aligned with your Director on top priorities
- Speed from lead capture to BDR follow\-up is measurably faster
- Clay and AI\-enabled workflows are live and improving segmentation, personalization, or campaign speed
- HubSpot runs cleanly with minimal manual workarounds
- Marketing, BDR, and Sales share clear lead definitions, reliable routing, and useful feedback loops
- Campaigns get faster to launch and easier to scale over time
What You Bring
- 5\+ years in growth operations, marketing operations, revenue operations, or a related field
- Hands\-on HubSpot expertise — workflows, lifecycle stages, lead scoring, routing, reporting
- Real curiosity and hands\-on experience applying AI or automation to marketing workflows — this matters as much as HubSpot depth
- Experience partnering with Sales, BDR, or Revenue Operations on lead handoff and routing
- Comfortable moving between strategic input and hands\-on execution, with a strong enough point of view to challenge assumptions rather than just take orders
- Strong analytical instincts — you look for root causes, not just symptoms
- Bonus: experience with Clay, Apollo, intent/enrichment data, or building AI\-enabled marketing workflows
Does this position sound like something you would enjoy and be successful at, but you're not sure you have the exact qualifications to be considered? While our job descriptions are an outline for the type of candidate we're looking for, they are not a checklist. We encourage you to apply!
This role is not open to candidates located in Colorado, Connecticut, California, Maryland, Nevada, New York, Rhode Island, or Washington.
Who is LeadVenture?
LeadVenture is the market\-leading SaaS provider of digital retailing, eCommerce, digital marketing and eCatalog solutions for dealerships across 12 industry verticals including power\-sports, marine, RV, pre\-owned auto, agriculture and more. Our family of brands includes Dealer Spike, Dealer Car Search, Frazer, TCS Technologies, Net Driven, Direct Communications, Inc. (DCi), Powersports Support, Level 5 Advertising, PSM Marketing, Monroney Labels and Interact RV. Each one is an industry leader in driving consumer engagement and maximizing lead generation for dealers. Our investors include the private equity firms True Wind Capital and TA Associates.
*LeadVenture provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, LeadVenture complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, transfer, leaves of absence, compensation, and training.*
*LeadVenture expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of LeadVenture employees to perform their job duties may result in discipline up to and including discharge.*
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LeadVenture, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
LeadVenture AI Hiring
LeadVenture has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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