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About This Role
Affinity stitches together billions of data points from massive datasets to create a powerful, accurate representation of the world's professional relationship graph. Based on this data, we offer our users the insights and visibility they need to nurture and tap into the opportunities in their team's network.
This role is part of the AI Platform team, which owns the AI services that power Affinity's industry\-leading relationship intelligence platform. We extract and retrieve information from billions of structured and unstructured data points to deliver actionable insights to customers.
As a Senior AI Engineer, you will collaborate with machine learning engineers, data engineers, software engineers, and product managers to shape the future of private capital's leading CRM platform. You will design and build LLM\-powered AI systems that efficiently uncover insights from compelling business interaction data – an exciting and unique opportunity within the industry.
In this role, you will:
- Build RAG systems: Architect, prototype, and deploy RAG pipelines, combining vector search, hybrid retrieval, reranking and contextual compression techniques.
- Build LLM\-powered agent systems: Contribute to design and orchestration of multi\-agent LLM systems using community frameworks and custom orchestration layers.
- Solve complex problems: Work on a variety of information extraction, information storage and information retrieval problems for both structured and unstructured data.
- Collaborate cross\-functionally: Partner with cross\-functional (product, infra, data engineering, and software engineering) teams to build robust, high\-scale systems that underlie all of our data processing and ML Operations.
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.
Required:
- 5\+ years of experience in software engineering and/or Machine Learning experience in applying machine learning in production.
- Hands\-on experience with LLM applications in production, including prompt engineering and utilizing frameworks for online and offline evaluation
- Experience with LLM\-assisted search, such as query understanding and augmentation, text2sql, and entity extraction
- Experience with vector or graph databases
- Experience with document chunking, embedding models, and context window optimization
- Familiarity with metadata\-based retrieval and re\-ranking strategies
- Hands\-on experiences with model evaluation metrics (e.g. perplexity, hallucination rate, factual consistency)
- Familiarity with data security, versioning, and MLOps principles
Nice to Have:
- Experience with enterprise AI applications with strict compliance, audit, or legal requirements
- Experience with dataset engineering, including data curation, augmentation, and synthesis, to assist ML model improvements.
- Experience with multi\-modal search
- Experience with graph\-based recommendation systems, such as graph NN.
- Experience with developing AI applications powered by agent\-based systems
- Experience with packaging, CI/CD and pipeline automation.
Tech stack: Our ML pipeline manages multiple Python services that support various AI features, including utilizing OCR to extract information from unstructured data, serving embedding models to vectorize chunks, and ranking a list of recommendations based on relevance and user preference.
How we work:
Our culture is a key part of how we operate, as well as our hiring process:
- We iterate quickly. As such, you must be comfortable embracing ambiguity, be able to cut through it, and deliver value to our customers.
- We are candid, transparent, and speak our minds while simultaneously caring personally with each person we interact with.
- We make data\-driven decisions and make the best decision for the moment based on the information available.
If you’d want to learn more about our values click here.
*Work Location:* *San Francisco, New York, or US Remote (Affinity is registered to employ in certain U.S. states)*
*For those located in San Francisco or New York, 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 your medical, dental, and vision insurance premiums with comprehensive PPO, HDHP and HMO options (in CA), and offer flexible personal \& sick days to support your well\-being.
- Retirement Planning: We offer a 401(k) 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 success.
Please note that the role compensation details below reflect the base salary only and do not include any equity or benefits. This represents the salary range that Affinity believes, in good faith, at the time of this posting, that it will pay for the posted job.
A reasonable estimate of the current range is $160,000 to $235,000 USD. 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.
Salary Context
This $160K-$235K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($197K) sits 10% below the category median. Disclosed range: $160K to $235K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Affinity AI Hiring
Affinity has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $235K - $235K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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