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ZipRecruiter
Santa Monica, United States5 days ago
200,000 – 265,000 USD
per year
Grade
Lead
Experience
from 5 years
Work format
Hybrid, Remote, Onsite
Employment
Full-time
English level
B2, upper-intermediate
An email or a Telegram message to the recruiter
To actively connect people to their next great opportunity.
Who We Are: ZipRecruiter is a leading online employment marketplace. Powered by AI-driven smart matching technology, the company actively connects millions of all-sized businesses and job seekers through innovative mobile, web, and email services, as well as through partnerships with the best job boards on the web. ZipRecruiter has the #1 rated job search app on iOS & Android.
About the Team: Recommendations Ecosystem The Recommendations Ecosystem group owns the intelligence that connects job seekers and jobs in our marketplace. We aim to understand every job and every job seeker well enough to make matches that work for both sides. We structure and enrich the jobs corpus, build the foundational models that encode jobs and job seekers, and run the matching engines that put the right opportunities in front of the right people.
What our team owns Our group is the authoritative source for job understanding and matching intelligence, focusing on accuracy, relevance, and trust: Jobs Corpus Understanding: ZipRecruiter takes in millions of job postings from employers, partners, and feeds. We own the ingestion and enrichment pipelines that turn raw postings into structured data. We use ML classifiers to infer job attributes like education requirements and pay, and we attach company and location data. We find and suppress fraudulent listings or terms of use violations, so the marketplace stays safe and worth using. The result is a canonical view of every job that recommendations, search, and employer products build on. Foundational Models and Embeddings: At the core of ZipRecruiter's matching ecosystem are models that encode everything we can learn about jobs and job seekers. We build LLM-based embedding models from job content, resumes, and behavioral signals. These are the building blocks for matching and other ML models across the company, and we serve them at scale online and in batch. Matching Engines: We are responsible for the ML systems that recommend jobs to job seekers and job seekers to employers across our app, website, and email. The engines cover large-scale candidate retrieval over vector DBs and orchestration that delivers fresh, personalized recommendations.
About the Job: ZipRecruiter’s Recommendation Ecosystem Group provides the backend machine learning and data platforms that connect job seekers to the right jobs. Within this group, the Job Understanding team is the authoritative source for foundational job data, pipelines, and matching intelligence. We are seeking an Engineering Manager to lead an experienced, high-impact engineering team.
In this role, you will guide the team in modernizing core data pipelines, storage solutions, and access methods while enhancing data quality and security systems.
We offer a hybrid work environment. Most US-based positions can also be performed remotely (any exceptions will be noted in the Minimum Qualifications below.)
The US base salary range for this full-time position is $200,000 - $265,000. Our salary ranges are determined by role, level, and location, and the range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location, role-related knowledge and skills, depth of experience, relevant education or training, and other role-related considerations. Depending on the position offered, equity, bonuses, commission, or other forms of compensation may also be provided as part of a total compensation package, in addition to a full range of medical, financial, and other benefits.
We offer a hybrid work environment. Most US-based positions can also be performed remotely (any exceptions will be noted in the Minimum Qualifications below.)
Our Mission: To actively connect people to their next great opportunity.
Who We Are: ZipRecruiter is a leading online employment marketplace. Powered by AI-driven smart matching technology, the company actively connects millions of all-sized businesses and job seekers through innovative mobile, web, and email services, as well as through partnerships with the best job boards on the web. ZipRecruiter has the #1 rated job search app on iOS & Android.
About the Team: Recommendations Ecosystem The Recommendations Ecosystem group owns the intelligence that connects job seekers and jobs in our marketplace. We aim to understand every job and every job seeker well enough to make matches that work for both sides. We structure and enrich the jobs corpus, build the foundational models that encode jobs and job seekers, and run the matching engines that put the right opportunities in front of the right people.
What our team owns Our group is the authoritative source for job understanding and matching intelligence, focusing on accuracy, relevance, and trust: Jobs Corpus Understanding: ZipRecruiter takes in millions of job postings from employers, partners, and feeds. We own the ingestion and enrichment pipelines that turn raw postings into structured data. We use ML classifiers to infer job attributes like education requirements and pay, and we attach company and location data. We find and suppress fraudulent listings or terms of use violations, so the marketplace stays safe and worth using. The result is a canonical view of every job that recommendations, search, and employer products build on. Foundational Models and Embeddings: At the core of ZipRecruiter's matching ecosystem are models that encode everything we can learn about jobs and job seekers. We build LLM-based embedding models from job content, resumes, and behavioral signals. These are the building blocks for matching and other ML models across the company, and we serve them at scale online and in batch. Matching Engines: We are responsible for the ML systems that recommend jobs to job seekers and job seekers to employers across our app, website, and email. The engines cover large-scale candidate retrieval over vector DBs and orchestration that delivers fresh, personalized recommendations.
About the Job: ZipRecruiter’s Recommendation Ecosystem Group provides the backend machine learning and data platforms that connect job seekers to the right jobs. Within this group, the Job Understanding team is the authoritative source for foundational job data, pipelines, and matching intelligence. We are seeking an Engineering Manager to lead an experienced, high-impact engineering team. In this role, you will guide the team in modernizing core data pipelines, storage solutions, and access methods while enhancing data quality and security systems.
Minimum Qualifications:
Preferred Qualifications:
As part of our team you’ll enjoy:
The US base salary range for this full-time position is $200,000 - $265,000. Our salary ranges are determined by role, level, and location, and the range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location, role-related knowledge and skills, depth of experience, relevant education or training, and other role-related considerations. Depending on the position offered, equity, bonuses, commission, or other forms of compensation may also be provided as part of a total compensation package, in addition to a full range of medical, financial, and other benefits.
ZipRecruiter is proud to be an equal opportunity employer and provides equal employment opportunities (EEO) to all employees and applicants without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity or genetics.
Privacy Notice: For information about ZipRecruiter's collection and processing of job applicant personal data for this job, please see our Privacy Notice.
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Most often 201,000 to 235,000
