Machine Learning Engineer

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.

Summary:

At ZipRecruiter, we sit on a massive universe of data—over a billion archived job postings, tens of millions of dynamic job seekers, and billions of impressions, clicks, and application events. Connecting the right job seeker with the right employer in real time is a complex two-sided marketplace problem, where precision, scale, and latent intent prediction directly impact millions of lives.

We are seeking a Staff Machine Learning Engineer / Data Scientist to serve as a technical anchor for our machine learning and AI capabilities. Reporting directly to the Director of Recommendation Systems, you will be a core partner in shaping our multi-year ML roadmap, driving foundational algorithmic architecture, and translating complex machine learning research into high-throughput, low-latency production systems.

This is a high-visibility role with org-wide reach. Beyond delivering core algorithmic gains, you will mentor Machine Learning Engineers across the organization and establish best practices for how ML models are built, deployed, and evaluated at scale.

Key Responsibilities & Strategic Impact

• Drive ML Strategy & Roadmap: Partner directly with Engineering and Product Leadership to define and execute the technical vision for core components in the marketplace, including but not limited to recommendation engines and matching algorithms, ML entity representation platform.
• Architect High-Scale Systems: Design and own state-of-the-art ML systems handling dynamic interaction prediction, candidate ranking, and candidate/job retrieval across high-throughput production environments.
• Optimize Two-Sided Marketplace Dynamics: Solve high-complexity matching and recommendation challenges native to two-sided marketplaces, including real-time intent prediction, bilateral relevancy, candidate cold-start problems, and feedback loops between job seekers and employers.