We’re building the next generation of AI evaluation systems — and we’re looking for a
motivated early-career engineer who’s excited to work at the intersection of ML,
software, and product. You’ll join a team focused on making AI systems — including
LLMs and agentic AI - more measurable, testable, and trustworthy in real-world
scenarios.
This is a hands-on, collaborative role ideal for someone with a strong foundation in
software engineering and machine learning, and an eagerness to grow by building tools
and systems that help evaluate advanced AI behavior at scale.
Description
As an Applied ML Engineer on our team, you’ll help develop simulation systems,
support data tooling, and contribute to evaluation workflows that improve the reliability
of modern AI. You’ll collaborate closely with experienced engineers and researchers,
learning how to instrument, monitor, and analyze model behavior — especially for
language models and agent-style systems.
This is a great opportunity for someone early in their career to work with cutting-edge AI
technologies in a high-impact, supportive environment. You’ll gain experience working
with large-scale ML systems, learn best practices in applied AI, and grow your skills
across engineering, product, and research.
Minimum Qualifications
Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field
Strong programming skills in Python or another modern language (e.g., Java, Swift,
Go)
Basic understanding of machine learning principles
Interest in LLMs, generative AI, or agent-based systems
Curiosity about how to evaluate and improve real-world AI performance
Strong collaboration and communication skills
Preferred Qualifications
Coursework or internship experience in ML, AI systems, or applied data science
Familiarity with training or evaluating models (even via coursework or personal
projects)
Exposure to tools like PyTorch, TensorFlow, or Hugging Face
Interest in AI observability, behavior simulation, or synthetic data
Passion for working cross-functionally in fast-moving, exploratory teams
Responsibilities
Contribute to systems that simulate interactive behaviors (including LLM-driven
agents)
Help build tools to support dataset generation and evaluation workflows
Assist in developing pipelines for structured insights from model behavior
Collaborate with teammates to debug and improve evaluation systems
Write clean, testable code to support scalable and reliable infrastructure
Learn how to define metrics that connect model behavior to real-world outcomes
Eeo Content
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
