Staff ML Engineer & Technical Leader - Production Systems

ziprecruiter; United States; Hybrid; USD 205,000 - 265,000; Full time

Benefits offered by this job

Competitive compensation

Exceptional benefits package

Flexible Vacation & Paid Time Off

Employer-matched 401(k) plan

Job summary

ZipRecruiter is seeking a Staff Machine Learning Engineer / Data Scientist to anchor our ML and AI capabilities. You will shape the multi-year roadmap, drive architecture, and translate advanced research into scalable production systems. This is a high-visibility role with org-wide impact.

You will mentor ML Engineers, lead end-to-end model ownership, and collaborate with Engineering and Product teams to improve the two-sided marketplace with efficient recommendation and retrieval pipelines.

Qualifications

• 8+ years of professional experience in ML in production environments.
• Proven track record shipping end-to-end ML solutions at scale.
• Deep domain expertise in Recommendation, Personalization, Ranking & Retrieval, or Interaction Prediction.

Responsibilities

• Define and execute the technical ML roadmap for core marketplace components.
• Architect high-scale ML systems for dynamic interaction prediction and candidate ranking.
• Lead ML model development from feature engineering through offline/online evaluation.
• Mentor ML Engineers and Data Scientists to raise technical excellence.
• Ensure production latency and reliability meet business needs.

Skills

Recommendation Systems

Mentorship

Distributed Systems

PyTorch

TensorFlow

Education

MS/PhD in CS/ML

Tools

MLOps

Distributed Training

Job description

ZipRecruiter is seeking a Staff Machine Learning Engineer / Data Scientist to anchor our ML and AI capabilities. You will shape the multi-year roadmap, drive architecture, and translate advanced research into scalable production systems. This is a high-visibility role with org-wide impact.

You will mentor ML Engineers, lead end-to-end model ownership, and collaborate with Engineering and Product teams to improve the two-sided marketplace with efficient recommendation and retrieval pipelines.