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About This Role
About the Role
We're building the next generation of email marketing tools at Validity. AI is central to how we build – we develop with agents daily – and to what we build, shipping AI\-powered features that help our customers succeed.
We're looking for a Principal Full Stack AI Engineer who ships fast, designs systems that last, and holds a high\-quality bar across a team that builds with AI every day.
Team Dynamic
We move fast, communicate directly, and debate ideas openly. The best idea wins regardless of who it comes from. We bias toward action – ship, learn, iterate.
Position Duties and Responsibilities* Quickly build and ship full\-stack features
- Design and deploy agentic AI systems that solve real user problems
- Work across multiple projects simultaneously, using agents to maintain velocity and context across concurrent workstreams
- Perform rigorous code review – especially of AI\-generated code – ensuring production\-grade quality, security, and systems design
- Set technical direction through architecture decisions, prototypes, and rapid experimentation
- Help define and improve evaluation for agent\-powered features
- Move from idea to production\-ready implementation at speed
- Work closely with product managers, shipping, learning, and iterating
Required Experience, Skills, and Education* 10\+ years of engineering experience shipping impactful products
- Deep full\-stack experience with React, Next.js, TypeScript, and PostgreSQL
- 2\+ years building agentic AI systems
- Demonstrated ability to work across multiple projects simultaneously, leveraging agents to multiply output
- Strong code review skills with the judgment to maintain high quality in an AI\-assisted development environment
- Strong product sense – you understand the product and what users want
- Excellent systems design – you architect for clarity, maintainability, and scale
- Bias toward action and rapid decision\-making
Who You Are* A doer – hands\-on with code and experiments daily
- A quality gate – you care about getting it right, and you know AI\-generated code needs sharp review
- Fast and intelligent – you learn fast, make smart decisions quickly, and iterate
- An AI\-native builder – agentic tools are part of how you work, not an afterthought
- Experimentation\-obsessed – you validate through building
- Comfortable with ambiguity – you forge ahead when the path isn't clear
- Collaborative – you thrive on code review and learning from others
- Hard\-working and motivated – you put in the effort and take pride in what you ship
Preferred Experience, Skills, and Education* History of 0\-to\-1 projects
Base salary range $200,000 \- $220,000, plus benefits, bonus opportunities and stock options. Final salary may vary depending on skills, location, and/or experience.About Validity
For over 20 years, tens of thousands of organizations across the world have relied on Validity solutions to target, contact, engage, and retain customers – using trustworthy data as a key advantage. Validity’s flagship products – Everest, DemandTools, BriteVerify, and GridBuddy Connect – are all highly rated, \#1 solutions for sales and marketing professionals. These solutions deliver smarter email campaigns, more qualified leads, more productive sales, and ultimately faster growth.
Validity is a truly unique company \- massive revenue growth, top\-tier investors, 5\-star product ratings, proven ability to acquire and integrate top tech companies and welcome them into the Validity family, a winning culture, and a work environment that fosters hard work, trust, and fun.
Headquartered in Boston, Validity has offices in Denver, London, Sao Paulo, and Sydney. For more information, connect with us on LinkedIn, Instagram, and Twitter.
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Validity is proud to be an equal opportunity employer. We are committed to providing equal employment opportunities to all employees and applicants for employment regardless of actual or perceived race, color, ancestry, national origin, citizenship, religion or creed, age, physical or mental disability, medical condition, AIDs/HIV status, genetic information, military and veteran status, sex, parental status (including pregnancy and pregnancy\-related conditions, childbirth, post childbirth, nursing mother, parent of a young child and parent of a foster child), gender (including gender identity and expression), sexual orientation, marital status (including registered domestic partner status), or any other characteristic protected by applicable federal, state, or local law.
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Salary Context
This $200K-$220K 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 Validity, 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. Disclosed range: $200K to $220K.
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.
Validity AI Hiring
Validity has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $120K - $220K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below 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 3,708 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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