What are the responsibilities and job description for the ML Research Engineer, AI Evaluation Platform position at Apple, Inc.?
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.\\n\\nWhat 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.
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.\n\nOn 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.\n\nWhile 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.
Bachelor's degree in Computer Science, Machine Learning, Software Engineering, or a closely related field (Master's preferred)\n2 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\nStrong proficiency in Python and the modern ML ecosystem (PyTorch, JAX, or TensorFlow), with demonstrated ability to implement complex methods from recent ML papers\nSolid software engineering fundamentals: clean code design, version control, testing, debugging, and performance optimization\nExperience working with large language models-whether fine-tuning, inference, prompting pipelines, or building LLM-powered applications\nDemonstrated 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\nPractical experience with cloud-native development and deployment: containerization (Docker/Kubernetes), CI/CD pipelines, and distributed computing frameworks (e.g., Ray, Spark)\nStrong communication skills and comfort working in interdisciplinary teams, with the ability to engage productively with both researchers and platform engineers\nComfort with ambiguity and new problem spaces-you thrive when building something that doesn't yet have a playbook
Master's or Ph.D. in Computer Science, Machine Learning, or a related field\nExperience with evaluation-specific methods or frameworks: LLM-as-judge approaches, reward modeling, RLHF, calibration techniques, benchmark design, or human evaluation methodology\nFamiliarity 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\nTrack record of contributing to research outputs-co-authored publications, open-source contributions, or internal research reports-even if research is not your primary role\nExperience 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\nFamiliarity with statistical concepts relevant to evaluation: calibration, inter-rater reliability, scoring rules, or measurement validity\nExperience in fast-moving, early-stage teams where you helped define technical direction and engineering culture from the ground up
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.\n\nOn 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.\n\nWhile 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.
Bachelor's degree in Computer Science, Machine Learning, Software Engineering, or a closely related field (Master's preferred)\n2 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\nStrong proficiency in Python and the modern ML ecosystem (PyTorch, JAX, or TensorFlow), with demonstrated ability to implement complex methods from recent ML papers\nSolid software engineering fundamentals: clean code design, version control, testing, debugging, and performance optimization\nExperience working with large language models-whether fine-tuning, inference, prompting pipelines, or building LLM-powered applications\nDemonstrated 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\nPractical experience with cloud-native development and deployment: containerization (Docker/Kubernetes), CI/CD pipelines, and distributed computing frameworks (e.g., Ray, Spark)\nStrong communication skills and comfort working in interdisciplinary teams, with the ability to engage productively with both researchers and platform engineers\nComfort with ambiguity and new problem spaces-you thrive when building something that doesn't yet have a playbook
Master's or Ph.D. in Computer Science, Machine Learning, or a related field\nExperience with evaluation-specific methods or frameworks: LLM-as-judge approaches, reward modeling, RLHF, calibration techniques, benchmark design, or human evaluation methodology\nFamiliarity 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\nTrack record of contributing to research outputs-co-authored publications, open-source contributions, or internal research reports-even if research is not your primary role\nExperience 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\nFamiliarity with statistical concepts relevant to evaluation: calibration, inter-rater reliability, scoring rules, or measurement validity\nExperience in fast-moving, early-stage teams where you helped define technical direction and engineering culture from the ground up