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About Us:
Pacvue is the leading Commerce Operating System, unifying retail media, commerce management, and advanced measurement across 100\+ global marketplaces, including Amazon, Walmart, Target, and Instacart. Through Pacvue and Helium 10, our enterprise and SMB platforms, we empower over 70,000 brands, agencies, and sellers with AI\-driven insights and technology to win in today's rapidly evolving commerce landscape.
At Pacvue and Helium 10, great careers don't just happen — they're built. Here, you'll rise as an industry leader, push the boundaries of innovation, own your journey, and thrive as part of an inclusive global community that's redefining the future of commerce.
About the role:
Pacvue is seeking a VP, AI Strategy \& Delivery to lead our AI strategy and execution across AI Labs, Pacvue Agent and Machine Learning. The role spans our entire AI remit, requiring a leader to possess outstanding technical skills, strong ability to engage/inspire customers, and a true talent builder.
Reporting to the Chief Product Officer, this leader will help scale Pacvue's global AI and Data Science organization as we continue to evolve into an AI\-first commerce media platform across retail media, DSP products, full\-funnel activation, measurement, optimization, and customer workflows.
This role will build on the strong AI and Data Science foundation already in place (based primarily in the US \& China), partnering closely with existing leaders and teams to sharpen strategy, increase customer impact, and scale AI capabilities across the platform.
This is a customer\-facing role. The VP of AI will serve as a key representative of Pacvue's AI strategy with customers, agencies, partners, and the broader commerce media ecosystem, helping customers understand how AI can improve media decisioning, workflow automation, measurement, optimization, and business outcomes.
Responsibilities:
*Represent Pacvue's AI Vision with Customers*
- Serve as a senior customer\-facing voice for Pacvue on AI, machine learning, Pacvue Agent, agentic workflows, automation, measurement intelligence, and the future of commerce media.
- Engage directly with enterprise brands, agencies, retailers, and strategic partners to understand their AI priorities, concerns, workflow gaps, and adoption barriers.
- Support executive briefings, customer advisory boards, strategic sales conversations, industry events, and thought leadership.
- Translate customer feedback into AI strategy, experimentation themes, product priorities, and roadmap decisions.
*Lead and Scale AI / Data Science*
- Lead Pacvue's AI, ML, and Data Science organization across experimentation, applied models, recommendations, optimization, measurement intelligence, forecasting, workflow automation, and agentic AI.
- Partner closely with existing AI and Data Science leaders to scale the organization, strengthen execution, and increase the impact of AI across customer\-facing products.
- Create operating clarity across AI Labs, production ML/AI, Data Science, Pacvue Agent, and agentic workflows, ensuring clear ownership, priorities, evaluation criteria, and product impact.
- Ensure our AI, ML and Data Science teams are aligned, focused and executing seamlessly. Building a world\-class AI team is paramount for our success and this leader must be as passionate about talent as they are the product and innovation they lead.
*Accelerate Pacvue Agent and Agentic AI*
- Accelerate Pacvue Agent as a core AI experience across the platform, directing the team, helping customers move from insight to action through trusted, explainable, workflow\-embedded AI.
- Lead Pacvue's agentic AI strategy across customer workflows, internal productivity, decision support, and governed automation.
- Define where AI should recommend, explain, act, escalate, and learn within customer and internal workflows.
- Partner with Product Growth and Customer Success to ensure AI capabilities are adopted, trusted, and connected to measurable customer outcomes.
*Own AI Labs and Experimentation*
- Lead AI Labs, focused on rapid experimentation, prototyping, and validation of emerging AI capabilities.
- Identify high\-potential AI use cases, test them quickly, and determine which should graduate into production roadmaps.
- Create a disciplined experimentation model that balances speed, customer value, feasibility, responsible AI practices, and business impact.
- Advance ML/AI and Data Science Capabilities
- Own the product and technical direction for ML, AI, and Data Science capabilities across recommendations, optimization, forecasting, measurement intelligence, workflow automation, and performance insights.
- Partner with Product, Engineering, and AI/DS leaders to turn models and AI capabilities into reliable, scalable, explainable product experiences.
- Drive decisions around model quality, explainability, trust, governance, evaluation, and adoption.
*Partner Across Product and GTM*
- Work closely with Media, Product Growth, Retail Media, Measurement, Platform, Engineering, Sales, Customer Success, and Marketing teams to bring AI into Pacvue's highest\-value product areas.
- Help translate AI capabilities into clear customer narratives, business value, packaging, and differentiated market positioning.
- Ensure AI is embedded into core product workflows rather than treated as a standalone feature or demo layer.
Skills \& Qualifications:
- 10\+ years of experience in AI, ML, Data Science, product leadership, enterprise software, ad tech, commerce media, or related fields, all at a global level.
- Experience leading AI, ML, or Data Science teams within a product\-led organization, with a track record of turning advanced technical capabilities into scalable customer\-facing products.
- Experience leading through senior technical and product leaders, with the ability to coach, amplify, and scale existing teams.
- Strong understanding of applied AI, machine learning, generative AI, agentic workflows, recommendation systems, optimization, forecasting, measurement, or decisioning platforms.
- Strong customer\-facing executive presence, with the ability to explain AI clearly to senior brand, agency, retailer, and partner stakeholders.
- Strong product judgment, including the ability to distinguish high\-value AI use cases from low\-value demos.
- Excellent communication skills, with the ability to make AI understandable, credible, and actionable.
- Willing to travel for customer facing events and meetings, as well as regular trips to China with meet with key members of our technical team.
Benefits:
- Flexible Paid Time Off
- Paid Holidays and Floating Holidays
- Medical, Dental, Vision, FSA, HSA with employer contribution, Life Insurance and Pet Insurance
- 401k with Employer Match
- Take up to 2 Days of Paid Time Off to Volunteer with a 501c Organization
- Paid Parental Leave
- Company\-subsidized membership to Wellhub and Peloton
- Fertility and family\-building support through Carrot
The annual base salary range for this position is $250,000\-$310,000 USD. The actual salary will vary depending on the applicant's experience, skills, and abilities as well as internal equity and market data for their location. This position is also eligible for an additional annual bonus compensation through one of Pacvue's highly attractive incentive plans, full details will be provided during the recruitment process.
\#LI\-Remote
*Pacvue is committed to employing a diverse workforce. Qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, or disability.*
Salary Context
This $250K-$310K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Pacvue, 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 $178,940 based on 11,900 positions with disclosed compensation. This role's midpoint ($280K) sits 56% above the category median. Disclosed range: $250K to $310K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Pacvue AI Hiring
Pacvue has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $310K - $310K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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