Software Engineer - AI, Evaluation

Apple Inc.
United States
4 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Data Retention Software Debugging Python (Programming Language) Machine Learning Software Engineering System Testing Large Language Models Generative AI AI Coding Agents Agentic-AI Information Technology
+2 more
Production Code Api Design

Job description

This is an ownership role. You will take on major systems in our evaluation platform, existing or new, and be accountable for its direction, quality, and adoption.

This role sits at the intersection of AI modeling, software engineering, and product quality.

We are expanding rapidly, so the ability to build a strong foundation and structure to any system is paramount. We are supporting features that encompass both product development and research, which requires a balance of respect for both the scientific process as well as the product release lifecycle.

Our systems connect many platforms and serve teams across Apple, so judgment with people matters as much as technical depth.

Requirements

BS/MS/PhD in Computer Science, Machine Learning, or a related field.

8+ years of software engineering experience, including owning production systems used by other teams.

Exceptional Python skills and a strong engineering understanding of system design, API design, system testing and validation, debugging and monitoring.

Experienced in shipping production code with AI coding agents while keeping quality high.

Preferred Qualifications

Proficient in building agentic systems, LLM-based applications, LLM-as-judge systems, and offline evaluations.

Ability to interpret evaluation results, including human agreement, run-to-run consistency, and respecting constraints like data retention and privacy policies.

Expert in building and maintaining technical integrations across multiple platforms in a large organization, with varying technical requirements and needs based off scaling, volume, throughput, etc.

Product-minded, turning ambiguous requirements into a focused plan.

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