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About This Role
The Company
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The Kantata Cloud for Professional Services™ gives businesses the clarity, control, and confidence they need to optimize resource planning and elevate operational performance. Our purpose\-built software is helping over 2,500 professional services organizations in more than 100 countries focus on and optimize their most important asset: their people. By leveraging Kantata, professionals gain access to the information and tools they need to win more business, ensure the right people are always available at the right time, and delight clients with exceptional project delivery and outcomes.
Kantata is well\-capitalized, hiring, and growing our loyal and diversified customer base faster than we ever have. Most importantly, we have a clear vision of where we’re going and how to get there. (Hint: It involves you.) Did we mention that Kantata is also an awesome place to work? You’ll have the opportunity to work in a dynamic environment with a team that loves what they do.
### About The Opportunity
Kantata is seeking a Paid Media Manager to lead the strategy, execution, and optimization of our global paid media programs. Reporting to the Sr Manager, Demand Generation, you will be the driving force behind our paid search, social, and display channels, ensuring we reach the right professional services leaders at the right stage of their journey.
In this role, you aren’t just "managing spend"—you are a growth architect. You will collaborate closely with Content, Design, Product Marketing, and Marketing Operations to build high\-converting campaigns that fuel our sales pipeline. We’re looking for a data\-driven specialist who thrives on experimentation, understands the nuances of B2B SaaS buyer journeys, and is eager to deliver measurable ROI.
### Primary Responsibilities
- Full\-Funnel Campaign Management: Develop and execute multi\-channel digital advertising strategies (Google Ads, LinkedIn, Bing, etc.) to drive brand awareness and high\-quality lead generation.
- Product Marketing Alignment: Partner closely with Product Marketing to translate product positioning, new feature launches, and persona\-based messaging into high\-performing ad campaigns.
- Budget \& Bid Optimization: Manage global advertising budgets, ensuring spend is allocated efficiently to hit monthly and quarterly Pipeline and CAC (Customer Acquisition Cost) targets.
- A/B Testing \& Experimentation: Constantly test ad copy, creative, and landing pages to improve CTR and conversion rates.
- Data Analysis \& Reporting: Monitor campaign performance daily. Build and maintain dashboards that communicate the impact of digital spend on ARR and sales velocity.
- Audience Segmentation: Leverage intent data and CRM insights to build sophisticated retargeting and Account\-Based Marketing (ABM) segments.
- Cross\-Functional Collaboration: Partner with the Content and Creative teams to ensure ad messaging aligns with brand voice and product updates.
- Stay Up\-to\-Date: Maintain a deep understanding of the evolving privacy landscape (cookies, tracking) and the latest features in the PSA / SaaS marketing space.
### What You Bring To This Role
- Proven Track Record: 5\+ years of experience managing complex paid media accounts, ideally within B2B SaaS.
- Strategic Collaboration: Experience working cross\-functionally with Product Marketing and Sales to align paid strategy with product roadmaps and revenue goals.
- Technical Expertise: Deep hands\-on experience with Google Ads, LinkedIn Campaign Manager, and Facebook/Meta Business Suite.
- Certified Specialist: Must hold current Google Ads Certifications (Search, Display, or Measurement).
- Analytical Mindset: Proficiency in Google Analytics 4 (GA4\) and experience using CRM data (Salesforce) to track the full lead\-to\-revenue lifecycle.
- Strong Communicator: Ability to translate complex data sets into actionable insights for executive leadership.
- Creative Intuition: A solid understanding of what makes a compelling B2B ad and how to guide creative teams toward high\-performing assets.
Ideal candidates will also have (Bonus):
- Professional Services Domain Knowledge: Familiarity with the Professional Services industry and the unique challenges faced by global consulting or embedded services teams.
- ABM Experience: Experience with Account\-Based Marketing platforms (e.g., 6sense, Demandbase, or Terminus).
Compensation:
- Base: $95,000 to $105,000 plus participation in company bonus plan
### Our Philosophy
### We know every company can be successful with the right technology and when people are at the core. We believe that we’re better together \- that working hand\-in\-hand brings the best thoughts to the table and creates an environment of learning and growth. Here, you’ll enjoy:
- An intentionally engaging and collaborative culture \- ditch the silo!
- Strong work\-life balance that’s a true focus of the company
- The chance to learn from some of the best people in the business
- A vibrant, collaborative, and devoted team, who still makes time for fun
At Kantata, we strive to create an inclusive workplace that upholds the dignity of all people. We value, respect and celebrate everyone’s unique strengths from all different walks of life. As we continue to cultivate diversity within the company, our product (and people!) innovation continues to flourish.
Kantata is an Equal Opportunity Employer.
Salary Context
This $95K-$105K 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 Kantata, 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 ($100K) sits 40% below the category median. Disclosed range: $95K to $105K.
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.
Kantata AI Hiring
Kantata has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $105K - $105K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 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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