Staff ML Engineer – Recommendation Systems (Remote)

Aimlroles; Santa Monica (CA); 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. Reporting to the Director of Recommendation Systems, you will shape the multi-year ML roadmap and translate research into production systems while mentoring ML engineers across the organization.

You will partner with Engineering and Product to build scalable recommendations, ranking, and retrieval solutions, focusing on low latency, high throughput, and rigorous experimentation.

Qualifications

• 8+ years of professional experience building and deploying ML models in production.
• Proven track record shipping end-to-end ML solutions serving production traffic at scale.
• Expertise in Recommendation Systems, Personalization, Ranking & Retrieval, or Interaction Prediction.
• Strong software engineering fundamentals with hands-on experience in PyTorch or TensorFlow.

Responsibilities

• Drive ML Strategy & Roadmap: define and execute the technical vision for core marketplace components.
• Architect High-Scale Systems: design and own ML systems for dynamic interaction prediction and ranking.
• Optimize Two-Sided Marketplace Dynamics: address real-time intent prediction and bilateral relevancy.
• Org-Wide Technical Leadership: mentor ML engineers and data scientists across teams.
• Production Excellence: end-to-end model ownership from feature engineering to online evaluation.

Skills

8+ years experience

Architecting ML solutions

Leadership & mentorship

Software engineering fundamentals

Education

MS/PhD in CS or related field

Experience with ML Ops

Tools

PyTorch

TensorFlow

Job description

ZipRecruiter is seeking a Staff Machine Learning Engineer / Data Scientist to anchor our ML and AI capabilities. Reporting to the Director of Recommendation Systems, you will shape the multi-year ML roadmap and translate research into production systems while mentoring ML engineers across the organization.

You will partner with Engineering and Product to build scalable recommendations, ranking, and retrieval solutions, focusing on low latency, high throughput, and rigorous experimentation.