Senior ML Engineer: Search & Knowledge Platforms

Socket.dev; Santa Clara (CA); On-site; USD 190,000 - 270,000; Full time

Job summary

Apple's AI, Search & Knowledge Platforms team builds world‑class products powering Siri, Apple Intelligence, Safari and iMessage. We are looking for a versatile Machine Learning and Software Engineer to improve the search and ranking stack with ML systems, robust heuristics, and LLMs.

You will tackle end‑to‑end challenges—from data pipelines and model training to prompt engineering and evaluation—while collaborating across teams to ship reliable, high‑quality knowledge answers that meet user

Qualifications

• Bachelor’s in Computer Science, AI, ML, or related field.
• 6+ years shipping Search and ML systems.
• Strong programming skills in Python, Go or similar languages.
• Experience building scalable data pipelines and backend systems.
• Proficiency in data science methodologies, including A/B testing and evaluation metrics for ML systems.
• Experience with prompt engineering.
• Strong product sense translating ambiguous product requirements into concrete tasks.
• Excellent communication with cross-functional partners.

Responsibilities

• Identify opportunities to improve search and ranking stack.
• Develop ML systems, heuristics, and LLMs to understand queries.
• Rank relevant documents and provide direct answers.
• Collaborate across teams to ship features and improvements.
• Evaluate models with offline and online metrics.

Skills

Python

Go

Data pipelines

ML systems

A/B testing

Prompt engineering

Product sense

Communication

Algorithms

Software design

Education

Bachelor's in CS/AI/ML

Master's/PhD in CS/AI/ML

Tools

RAG architectures

LLM deployment tooling

NLP libraries

Job description

Apple's AI, Search & Knowledge Platforms team builds world‑class products powering Siri, Apple Intelligence, Safari and iMessage. We are looking for a versatile Machine Learning and Software Engineer to improve the search and ranking stack with ML systems, robust heuristics, and LLMs.

You will tackle end‑to‑end challenges—from data pipelines and model training to prompt engineering and evaluation—while collaborating across teams to ship reliable, high‑quality knowledge answers that meet user