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About This Role
Are you ready to be a big part of something big?
At Trader Interactive, we make buying and selling a great experience. We’re a group of go\-getters who decided they didn’t want to settle for the status quo. We come together as one team to build value and drive innovation across our industries \- but we have fun while we do it and make sure our people are always our \#1 priority.
When it comes to your career, we want to provide big opportunities to help you make a big impact. But for this to be possible, we strive to feel small. Small enough to quickly change tack, small enough to learn from different teams and small enough to connect authentically with leadership.
And one of the best parts? We give you the freedom to work from whatever working location works best for you and your lifestyle \- yes, this means you can be 100% remote if you want to be!
What We Offer
- An inclusive and supportive work environment where you can move your career forward and will have the chance to do work that has real, significant impact on the world.
- The opportunity to be a part of a global group of digital marketplace businesses (CAR Group) located across Australia, Brazil, Chile and South Korea \- collectively we have around 2,500 team members worldwide, and our CAR Group Tour means you might just find yourself working in one of those businesses sometime soon.
- Plenty of flexible leave options and employee benefits including up to 31 days of paid time off in your first year, continuing education with access to LinkedIn Learning, a full benefits package including medical, dental \& vision, 401K with company match, and wellness program.
What You’ll Do:
The Matador Product Specialist is a quota\-carrying, new\-business hunter responsible for driving net new logo acquisition across Commercial Truck, Marine, Equipment and Recreational vertical markets. This is a full\-cycle sales role; from prospecting and pipeline generation through discovery, demonstration, negotiation and close — selling Conversational AI solutions to dealership groups and operators.
Working in concert with SDR support, channel and referral partnerships and self\-sourced outbound activity, the Product Specialist owns revenue growth in their assigned territory. This is a high\-impact, individual contributor role modeled after best\-in\-class SaaS acquisition motions seen at large enterprise SaaS companies — structured for reps who thrive in complex, consultative sales environments and are energized by building new customer relationships.
Pipeline Generation \& Prospecting
- Execute a balanced prospecting motion across self\-sourced outbound, SDR qualified inbound, and channel/referral\-sourced leads.
- Build and maintain a minimum 3x pipeline coverage ratio against quota at all times.
- Develop territory and account\-level prospecting plans targeting Commercial (powersports, RV, marine, construction equipment) and Recreational verticals.
- Leverage referral networks within the broader Trader Interactive ecosystem to identify cross\-sell and co\-sell opportunities.
- Partner closely with SDR team to refine messaging, qualify inbound interest and convert MQLs to SQLs efficiently.
Sales Execution \& Deal Management
- Own the full sales cycle from initial discovery call through signed contract, typically 60\-120 days depending on deal complexity.
- Conduct compelling discovery sessions to understand dealership workflow, technology gaps and performance objectives.
- Deliver tailored product demonstrations of Matador Conversational AI tools to dealership operators, GMs, and ownership groups.
- Build multi\-threaded relationships with economic buyers, champions and end users within prospect accounts.
- Develop and deliver ROI\-based business cases and proposals aligned to each prospect's operational metrics and KPIs.
- Manage deal progression in CRM with accurate forecasting, stage discipline and pipeline hygiene at all times.
Channel \& Partner Collaboration
- Develop and nurture relationships with referral partners, OEM contacts and adjacent Trader Interactive business units to drive inbound deal flow.
- Develop and nurture relationships with referral partners, OEM contacts and adjacent Trader Interactive business units to drive inbound deal flow.
- Coordinate with channel partners on joint pipeline reviews, co\-selling motions and shared account strategies.
- Act as a Matador brand ambassador at industry events, dealer associations and trade shows within target verticals.
Cross\-Functional Alignment
- Feed competitive intelligence, objection patterns and prospect feedback to Product, Marketing,
- Marketplace Sales and Leadership teams on a structured basis.
- Support go\-to\-market initiatives including beta launches, product releases and vertical\-specific marketing campaigns.
- Partner with Marketing to refine ICP targeting, messaging and campaign effectiveness based on field insights.
What We’re Looking For:
- 3–5\+ years of quota\-carrying SaaS sales experience in a full\-cycle Account Executive or equivalent role.
- Demonstrated track record of consistent quota attainment at or above 100%.
- Experience selling operational or vertical SaaS solutions to SMB and mid\-market customers ($15K–$75K ACV).
- Strong command of consultative selling methodologies (Challenger or equivalent).
- Proven ability to self\-source pipeline through outbound prospecting in addition to working inbound and partner\-referred leads.
- Excellent discovery, presentation, and negotiation skills with comfort presenting to C\-suite and ownership\-level buyers.
- Data\-literate and CRM\-disciplined — experience maintaining accurate pipeline in Salesforce, HubSpot or equivalent.
Bonus Points:
- Experience selling into automotive, powersports, RV, marine, or equipment dealership verticals.
- Familiarity with CRM platforms, dealership management systems (DMS), or Conversational AI/messaging products.
- Background selling within a SaaS portfolio (e.g., Coupa, Anaplan, DealerSocket, VinSolutions, vAuto).
- Experience working in a channel\-assisted or partner co\-sell sales environment.
- Comfort with product\-led growth motions and shorter, transactional deal cycles alongside complex enterprise sales.
So come and join our team \- because every role is a big role in our plans to go big.
TI proudly supports a diverse workforce, and we encourage candidates from underrepresented groups to apply. Trader Interactive is an equal opportunity employer where hiring is based entirely on business needs, job requirements, and individual merit.
Salary Context
This $15K-$75K range is in the lower quartile 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 Trader Interactive, 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 ($45K) sits 73% below the category median. Disclosed range: $15K to $75K.
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.
Trader Interactive AI Hiring
Trader Interactive has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Virginia Beach, VA, US. Compensation range: $75K - $75K.
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.
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