ML Researcher

Apple • Seattle, WA

5h

About The Position

AI systems are only as trustworthy as the methods used to evaluate them. At
Apple, where AI powers experiences for billions of people, getting evaluation
right is not a support function—it is a foundational science. Our team, part of
Apple Services Engineering, is building that scientific foundation: rigorous,
scalable evaluation methodology for LLMs, agentic systems, and human-AI
interaction. What makes this team unusual is its interdisciplinary core. You
will work alongside measurement scientists (psychometrics, validity theory), ML
researchers, and platform engineers—bringing together ML research, statistical
rigor, and production engineering. We are looking for an ML Research Engineer
who can move fluidly across this landscape: someone who loves implementing the
latest techniques in AI, has the engineering instincts to make them robust and
scalable, and thrives at the intersection of research and production.

DESCRIPTION

This is a combined research and engineering role, sitting with and between
research/applied scientists and platform engineers. New evaluation research can
be challenging to use at scale—that's where your skills in both machine learning
and engineering come into play. On the research side, you will partner with
scientists to rapidly prototype their ideas, implement methods from recent
papers, run large-scale experiments, and provide critical feedback grounded in
your engineering experience. On the engineering side, you will work with
platform engineers to bring those research prototypes into production—moving
from Python packages on local machines to robust services deployed in the cloud.
While past experience in research is not required, a desire to advance the state
of the art in AI evaluation is. You should be ready to jump in across the full
lifecycle of bringing new research into production at scale, speaking both the
language of research and the language of engineering.

Requirements

• Bachelor's degree in Computer Science, Machine Learning, Software Engineering, or a closely related field (Master's preferred)
• 2+ years of hands-on experience in a role combining machine learning and software engineering (e.g., ML engineer, research engineer, or applied scientist with strong engineering output), or a Master's degree in Computer Science, Machine Learning, or a closely related field with relevant project experience
• Strong proficiency in Python and the modern ML ecosystem (PyTorch, JAX, or TensorFlow), with demonstrated ability to implement complex methods from recent ML papers
• Solid software engineering fundamentals: clean code design, version control, testing, debugging, and performance optimization
• Experience working with large language models—whether fine-tuning, inference, prompting pipelines, or building LLM-powered applications
• Demonstrated ability to work across the research-to-production spectrum: you have taken experimental or prototype code and made it robust, scalable, and usable by others
• Practical experience with cloud-native development and deployment: containerization (Docker/Kubernetes), CI/CD pipelines, and distributed computing frameworks (e.g., Ray, Spark)
• Strong communication skills and comfort working in interdisciplinary teams, with the ability to engage productively with both researchers and platform engineers
• Comfort with ambiguity and new problem spaces—you thrive when building something that doesn't yet have a playbook

Nice To Haves

• Master's or Ph.D. in Computer Science, Machine Learning, or a related field
• Experience with evaluation-specific methods or frameworks: LLM-as-judge approaches, reward modeling, RLHF, calibration techniques, benchmark design, or human evaluation methodology
• Familiarity with modern evaluation tools and frameworks (e.g., DeepEval, Ragas, TruLens, LangSmith) and an understanding of how to implement and scale model-based evaluation workflows
• Track record of contributing to research outputs—co-authored publications, open-source contributions, or internal research reports—even if research is not your primary role
• Experience with the engineering challenges specific to generative AI and agentic systems: managing token economics, handling non-deterministic outputs, evaluating multi-turn agent trajectories and tool usage
• Familiarity with statistical concepts relevant to evaluation: calibration, inter-rater reliability, scoring rules, or measurement validity
• Experience in fast-moving, early-stage teams where you helped define technical direction and engineering culture from the ground up

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees