Interested in this AI/ML Engineer role at Affinity?
Apply Now →Skills & Technologies
About This Role
Affinity is the relationship intelligence CRM trusted by private equity, venture capital, and investment banking professionals to manage their most valuable asset: relationships. Our platform transforms how deal teams source opportunities, manage pipelines, and accelerate deal flow by automatically capturing and organizing relationship data. With our 72\-hour implementation and AI\-powered insights, we're redefining what's possible in private capital markets.
The Role
------------
We're seeking a performance\-driven Paid Media Manager to lead our full\-funnel paid media strategy and drive measurable growth across B2B channels. This role combines strategic thinking with hands\-on execution, focusing on continuous testing, optimization, and AI\-powered innovation. You'll own multi\-million dollar budgets while pioneering new channel opportunities in the competitive private capital software space. This role reports to the VP of Demand Generation.
What will I be doing?
-------------------------
Strategic Planning \& Execution
- Develop and execute comprehensive paid media strategies across the full funnel from awareness to conversion
- Own multi\-channel campaign planning, budget allocation, and performance optimization across search, social, display, and emerging channels
- Lead quarterly and annual media planning aligned with revenue goals and pipeline targets
- Drive account\-based marketing (ABM) campaigns targeting high\-value private equity and venture capital prospects
Channel Management \& Optimization
- Manage and optimize campaigns across Google Ads, LinkedIn Ads, Meta, Reddit, Connected TV, Display/Programmatic, and emerging B2B channels
- Continuously test new channels and ad formats to expand reach within private capital markets
- Implement advanced targeting strategies including account\-based targeting, lookalike audiences, and intent\-based targeting
- Execute sophisticated bid management and budget optimization strategies to maximize ROAS
AI\-Powered Innovation
- Leverage AI tools daily for audience research, creative optimization, bid management, and performance analysis
- Implement AI\-driven creative testing frameworks for ad copy, headlines, and visual assets
- Use machine learning tools for predictive audience modeling and campaign optimization
Testing \& Growth Experimentation
- Design and execute rigorous A/B and multivariate testing programs across all channels
- Test new ad formats, bidding strategies, audience segments, and creative approaches
- Implement incrementality testing and attribution modeling to measure true campaign impact
- Pioneer testing in emerging channels and platforms relevant to B2B audiences
Analytics \& Performance Management
- Own paid media KPIs including CAC, ROAS, pipeline contribution, and channel efficiency metrics
- Build comprehensive reporting dashboards and provide actionable insights to leadership
- Conduct deep\-dive analysis on campaign performance, audience behavior, and competitive positioning
How You'll Work
-------------------
- Think full\-funnel – understand how paid media drives awareness, consideration, and conversion across the buyer journey
- Operate with precision – manage budgets efficiently while maintaining aggressive growth targets
- Collaborate seamlessly with Product Marketing, Sales, Marketing Operations, and Creative teams
- Stay ahead of trends – constantly evaluate new platforms, ad formats, and optimization techniques
- Leverage data obsessively – make decisions based on performance data, not intuition
Qualifications
------------------
Don't meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every qualification. At Affinity, we are dedicated to building a diverse, inclusive, and authentic workplace, so if you're excited about this role but your past experience doesn't perfectly align with the qualifications above, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
- 5\-7 years of B2B paid media experience, preferably in SaaS, fintech, or high\-ACV software environments
- Proven track record managing annual paid media budgets of $2M\+ with demonstrated ROI improvement
- Platform expertise across Google Ads, LinkedIn Ads, Meta Business Manager, Reddit Ads, Connected TV platforms, and programmatic platforms
- Advanced analytics skills with proficiency in Google Analytics, attribution platforms, and data visualization tools
Technical Proficiency
- Daily AI tool usage for campaign optimization, creative testing, and audience research
- Advanced Excel/Google Sheets skills for budget management and performance analysis
- Understanding of B2B marketing metrics including pipeline attribution, CAC payback, and lifetime value
Strategic Thinking
- Full\-funnel mindset with ability to connect top\-funnel activities to bottom\-line revenue impact
- Testing methodology expertise including statistical significance, incrementality testing, and experiment design
- Competitive analysis skills with ability to monitor and respond to competitor media strategies
- Budget optimization experience with ability to allocate spend across channels for maximum efficiency
Industry Knowledge
- Understanding of B2B buying cycles and account\-based marketing principles
- Familiarity with private capital markets preferred but not required
- Knowledge of privacy regulations and their impact on digital advertising (GDPR, CCPA, iOS changes)
- Awareness of emerging channels and willingness to test new opportunities
Why This Role Matters
-------------------------
You'll be instrumental in scaling Affinity's growth by building a world\-class paid media engine that efficiently acquires high\-value customers in the competitive private capital software market. Your expertise in testing, optimization, and AI\-powered innovation will directly impact our ability to capture market share and drive sustainable revenue growth.
This is a high\-impact role where your strategic thinking and execution excellence will be visible at the highest levels of the organization, with direct influence on company growth and market positioning.
Work Location: Remote or Toronto, Canada
*For those located in Toronto, for this role we're embracing a hub\-hybrid model, designed to balance flexibility with meaningful in\-person collaboration. Team members within commuting distance are expected in\-office 2–3 days per week, typically Tuesday through Thursday. We believe great things happen when people come together intentionally to connect, create, and build momentum as a team.*
What you'll enjoy at Affinity:
- We live our values: As owners, we take pride in everything we do. We embrace a growth mindset, engage in respectful candor, act as playmakers, and "taste the soup" by diving deep into experiences to create the best outcomes for our colleagues and clients.
- Health Benefits: We cover both you and your dependents' extended health benefit premiums and offer flexible personal \& sick days to support your well\-being.
- Retirement Planning: We offer an RRSP plan to help you plan for your future.
- Learning \& Development: We provide an annual education budget and a comprehensive L\&D program.
- Wellness Support: We reimburse monthly for things like home internet, meals, and wellness memberships/equipment to support your overall health and happiness.
- Team Connection: Virtual team\-building activities and socials to keep our team connected, because building strong relationships is key to succes.
A reasonable estimate of the current range is $91,200\.00 \- $114,000\.00 CAD Base. Within the range, individual pay depends on various factors including geographical location and review of experience, knowledge, skills, abilities of the applicant.
About Affinity
With more than 3,000 customers worldwide and backed by some of Silicon Valley's best firms, Affinity has raised $120M to empower dealmakers to find, manage, and close more deals. How? Our Relationship Intelligence platform uses the wealth of data exhaust from trillions of interactions between Investment Bankers, Venture Capitalists, Consultants, and other strategic dealmakers to deliver automated relationship insights that drive over 450,000 deals every month. We are are proud to have received Inc. and Fortune Best Workplaces awards as well as to be Great Places to Work certified for the last 5 years running. Join us on our mission to make it possible for anyone to cultivate and fully harness their network to succeed.
We use E\-Verify
Our company uses E\-Verify to confirm the employment eligibility of all newly hired employees. To learn more about E\-Verify, including your rights and responsibilities, please visit www.dhs.gov/E\-Verify.
Salary Context
This $91K-$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 Affinity, 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 ($102K) sits 39% below the category median. Disclosed range: $91K 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.
Affinity AI Hiring
Affinity has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Ontario, CA, US. Compensation range: $114K - $133K.
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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.