Machine Learning Engineer

$128K - $201K Boston, MA, US Mid Level AI/ML Engineer

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Skills & Technologies

EmbeddingsPythonPytorchZoominfo

About This Role

AI job market dashboard showing open roles by category

ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You'll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won't just contribute. You'll make things happen–fast.

You'll build the intelligence every ZoomInfo AI agent reasons over — what's true about companies and the people in them, how they relate, and what they're buying. As a Machine Learning Engineer, you will own outcomes end-to-end on the team extending ZoomInfo's B2B data graph into the long tail of companies with little public information, resolving entity identity at scale, and reading buying intent from meaning rather than keywords. You will apply classical machine learning, statistics, or modern language models, whichever the problem calls for, with a short path from decision to production.

What You'll Do

  • You will extend ZoomInfo's data graph into the long tail of companies with little public footprint, extracting leadership, locations, and products from company websites and detecting stale records.
  • You will predict what the graph doesn't know yet, estimating headcount and revenue for under-documented companies using gradient-boosted trees, regression with missing inputs, and calibrated uncertainty.
  • You will determine whether two records describe the same company or person, measuring both wrongly merged and wrongly split outcomes.
  • You will infer buying intent from the meaning of web content across large volumes of multilingual, noisy text, feeding propensity scoring, lookalike retrieval, and contact recommendations.
  • You will build agents that research companies and cite their sources, and design the evaluations that separate a correct result from a run that merely finished.
  • You will distill large models into smaller ones that run cost-effectively across the full dataset, owning quantization and serving as part of the same work.
  • You will take ambiguous, high-stakes problems from undefined to shipped, and raise the team's engineering bar through review, design, and mentorship.

What You Bring

Must-Have:

  • You have taken machine learning systems to production and owned them after launch, following the outcome into whichever layer it needs, with technical leadership as an individual contributor: setting direction for a problem area, design review, and mentoring. Depth matters more than years.
  • You bring classical machine learning expertise beyond language models, including supervised learning and feature engineering on large, messy tabular data.
  • You apply statistics to decisions: experiment design, statistical inference, and calibrated scores under class imbalance.
  • You have deployed language processing at scale — text classification, information extraction, and entity linking over large volumes of multilingual, noisy text.
  • You have built and operated LLM agents or multi-step systems in production, including tool and context design and failure analysis from traces, with evaluation for systems with no single right answer using LLM judges validated against human labels.
  • You are proficient in production Python and strong SQL with distributed data processing experience, and you use AI coding tools daily with rigorous review of their output.

Preferred:

  • You have experience with ranking and retrieval, including embeddings, learned re-ranking, and metrics such as recall@k, MRR, and nDCG.
  • You bring propensity modeling, clustering, or entity resolution experience on messy, real-world data.
  • You have trained and served open-weight models in PyTorch or an equivalent framework, tracking cost per unit of work.
  • You have defended LLM systems against adversarial inputs and prompt injection, or worked on web-scale information extraction, knowledge graphs, or user memory for agents.

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About us:

ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.

ZoomInfo is committed to protecting your privacy when you apply for jobs with us. Please review our Job Applicant Privacy Notice for more details on how we handle your personal information.

ZoomInfo may use a software-based assessment as part of the recruitment process. More information about this tool, including the results of the most recent bias audit, is available here.

ZoomInfo is proud to be an equal opportunity employer, hiring based on qualifications, merit, and business needs, and does not discriminate based on protected status. We welcome all applicants and are committed to providing equal employment opportunities regardless of sex, race, age, color, national origin, sexual orientation, gender identity, marital status, disability status, religion, protected military or veteran status, medical condition, or any other characteristic protected by applicable law. We also consider qualified candidates with criminal histories in accordance with legal requirements.

For Massachusetts Applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. ZoomInfo does not administer lie detector tests to applicants in any location.

Salary Context

This $128K-$201K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).

View full AI/ML Engineer salary data →

Role Details

Company ZoomInfo
Title Machine Learning Engineer
Location Boston, MA, US
Category AI/ML Engineer
Experience Mid Level
Salary $128K - $201K
Remote No

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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At ZoomInfo, 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

Embeddings (2% of roles) Python (17% of roles) Pytorch (5% of roles) Zoominfo

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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($164K) sits 22% below the category median. Disclosed range: $128K to $201K.

Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.

ZoomInfo AI Hiring

ZoomInfo has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Waltham, MA. Compensation range: $201K - $366K.

Location Context

AI roles in Boston pay a median of $215,000 across 323 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.

The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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

Based on 11,857 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $212,000. Actual compensation varies by seniority, location, and company stage.
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.
About 6% of the 9,653 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
ZoomInfo is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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