The posting
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.
- Lead, mentor, and unblock a senior team of 5 engineers responsible for core job ingestion pipelines and data stores.
- Drive the rebuild of the job ingestion pipeline and refine how job data is exposed to consuming engineering teams.
- Prioritize initiatives that improve underlying input data quality and handle low-structure, user-generated content.
- Own and lead improvements to fraud detection and blocking mechanisms.
- Partner closely with other Engineering stakeholders, Data Scientists, and a dedicated Product Manager to balance requests and roadmap priorities.
- Ensure we can act on everything we learn about jobs in a scalable real time manner. Scale our data pipelines both for performance and maintainability so we can be ambitious about what data we collect and where we can personalize the job seeker experience.
Minimum Qualifications:
- 5+ years of professional software development experience.
- 2+ years of experience as a software engineering leader
- Deep experience with modern big data frameworks (e.g. Spark, Hadoop, Hive).
- Streaming technologies (Kafka, Flink, Beam).
- Strong CS fundamentals (data structures, algorithms) and coding skills.
- Experience with containerization technologies like Docker and Kubernetes.
- Experience working on a B2C or B2B Product
- This role is for engineers who are talking with their Product Managers about problems to solve and turning them into engineering solutions. They are naturally curious, problem solvers, and deeply understand the product they work on.
- Must have experience dealing with messy/unreliable data (third party data sets, data integrations, migrations)
Preferred Qualifications:
- Cloud platforms (GCP, AWS, Azure).
- Modern data storage solutions (Delta Lake, HBase, Hudi).
- Scala
As part of our team you’ll enjoy:
- Competitive compensation
- Exceptional benefits package
- Flexible Vacation & Paid Time Off
- Employer-matched 401(k) plan
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 additional 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 at: https://www.ziprecruiter.com/careers/job-applicant-privacy-notice
