Senior ML Engineer: Scale ML for Search & Ranking

Apple Inc.; Seattle (WA); On-site; USD 185,000 - 325,000; Full time

Benefits offered by this job

Medical and dental coverage

Employee stock plans

Relocation assistance

Tuition reimbursement

Job summary

Apple Inc. is seeking an experienced ML engineer to join the Maps/Local search domain in Santa Clara, CA. You will own the ML development lifecycle, build retrieval and ranking models, and lead experiments to improve local search quality.

The role emphasizes applying ML techniques such as embedding models and learning-to-rank, mentoring engineers, and translating product needs into ML tasks in a fast-paced environment.

Qualifications

• 8+ years of experience in information retrieval, NLP, ML or deep learning.
• Deep understanding of ML theory, including supervised learning, ranking models, embeddings and evaluation metrics.
• Proven ability to apply advanced ML techniques to improve search relevance and retrieval quality at scale.
• Comfortable leading experimentation, offline evaluation and online A/B testing.
• Excellent interpersonal skills and ability to work both independently and in a team.

Responsibilities

• Own the entire ML development cycle from opportunity analysis to data collection and deployment.
• Lead the development of ML models to improve search quality across retrieval, ranking, reranking and query understanding.
• Improve search quality using learning-to-rank, embeddings, contrastive learning and related methods.
• Mentor junior engineers and provide technical leadership in ML systems and models.
• Translate product requirements into modeling and engineering tasks.

Skills

Information retrieval

Natural Language Processing

Machine Learning

Deep Learning

Experimentation leadership

Education

Master's Degree in Computer Science or equivalent

Advanced diploma in AI/ML/CS/Mathematics

Job description

Apple Inc. is seeking an experienced ML engineer to join the Maps/Local search domain in Santa Clara, CA. You will own the ML development lifecycle, build retrieval and ranking models, and lead experiments to improve local search quality.

The role emphasizes applying ML techniques such as embedding models and learning-to-rank, mentoring engineers, and translating product needs into ML tasks in a fast-paced environment.