Remote Staff ML Engineer — Scalable Recommendations

Ziprecruiter; Santa Monica (CA); Hybrid; USD 205,000 - 265,000; Full time

Benefits offered by this job

Competitive pay

Benefits package

Flexible vacation

401(k) plan

Job summary

ZipRecruiter is seeking a Staff Machine Learning Engineer / Data Scientist to lead ML strategy and architecture for a two-sided marketplace. You will drive scalable systems, mentor engineers, and translate research into production-ready solutions that power matching and recommendations.

The role reports to the Director of Recommendation Systems and requires extensive experience in ML, ranking, and experimentation, with a focus on delivering low-latency production models in a hybrid work

Qualifications

• 8+ years of professional experience developing and deploying ML models in production.
• Expertise in building and shipping end-to-end ML solutions deployed at scale.
• Strong background in Recommendation Systems, Personalization, Ranking/ Retrieval, or Interaction Prediction.

Responsibilities

• Define and drive ML strategy and roadmap for core marketplace components.
• Architect high-scale ML systems for dynamic interaction prediction and candidate retrieval.
• Tackle two-sided marketplace challenges including bilateral relevancy and cold-start problems.
• Mentor ML Engineers and Data Scientists to establish best practices for production ML.
• Own end-to-end model lifecycle from exploration to online/offline evaluation and latency optimization.

Skills

ML experience

Recommendation systems

Leadership

A/B testing

Education

MS/PhD in CS or related

Tools

PyTorch

TensorFlow

MLOps

Job description

ZipRecruiter is seeking a Staff Machine Learning Engineer / Data Scientist to lead ML strategy and architecture for a two-sided marketplace. You will drive scalable systems, mentor engineers, and translate research into production-ready solutions that power matching and recommendations.

The role reports to the Director of Recommendation Systems and requires extensive experience in ML, ranking, and experimentation, with a focus on delivering low-latency production models in a hybrid work