Staff Machine Learning Engineer, Recommendations

We offer a hybrid work environment. Most US-based positions can alsobeperformed 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. - **Org-Wide Technical Leadership:** Mentor and guide Machine Learning Engineers and Data Scientists across teams to instill a culture of technical excellence, rigorous experimentation, and fast production delivery. - **Production Excellence:** Drive end-to-end model ownership—from initial exploration and feature engineering through distributed training, offline/online evaluation (A/B testing), to real-time latency optimization. ### **Minimum Qualifications** - **8+ years** of professional experience developing and deploying machine learning models in large-scale production environments. - Proven track record of **architecting and shipping end-to-end ML solutions** that serve production traffic at scale. - Deep domain expertise in **Recommendation Systems, Personalization, Ranking & Retrieval, or Interaction Prediction**. - Strong software engineering fundamentals with hands-on expertise using modern deep learning frameworks (PyTorch, TensorFlow). - Proven experience in **technical leadership and mentorship**, driving technical alignment across cross-functional engineering and product teams. - Strong background in statistical modeling, online experimentation (A/B testing methodology), and offline metric design. ### **Preferred Qualifications** - **Experience in Two-Sided Marketplaces:** Familiarity with supply/demand liquidity, bilateral matching algorithms, dynamic pricing, or auction-based models. - Modern deep learning techniques for recommendations, such as **Two-Tower Neural Networks, Graph Neural Networks (GNNs), Transformer-based retrieval models, or Contextual Bandits**. - Advanced degree (MS/PhD) in Computer Science, Machine Learning or a related quantitative field or equivalent experience. - Experience with modern MLOps architectures and distributed training frameworks. **As part of our team you’ll enjoy:** - Competitive compensation - Exceptional benefits package - Flexible Vacation & Paid Time Off - Employer-matched 401(k) plan #LI-Remote The US base salary range for this full-time position is $205,000.00-$265,000.00 USD. 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. 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