Machine Learning Engineer AI Evaluation & LLM Systems

Apple


Job Location:

Cupertino, CA - USA

Monthly Salary: Not Disclosed
Posted on: 15 hours ago
Vacancies: 1 Vacancy

Job Summary

Join the team building the evaluation systems that enable Apples next generation of AI experiences. As a Machine Learning Engineer you will develop scalable infrastructure intelligent evaluators and data-driven methodologies that measure and improve the quality of large language models and multimodal AI systems used across Apple partner closely with ML researchers software engineers and product teams to design novel evaluation techniques analyze model behavior and translate research into production-ready systems. This role requires strong engineering fundamentals a passion for machine learning and the curiosity to solve challenging problems at the intersection of AI data and software youre excited about building the tools that help define the future of AI quality at Apple wed love to hear from you.

As a Machine Learning Engineer you will build the systems that measure and improve the quality of AI experiences used by millions of people. You will develop machine learning models evaluation frameworks and scalable infrastructure that enable teams to understand model behavior identify regressions and accelerate the development of large language models and multimodal AI. Working closely with researchers software engineers and product teams you will transform cutting-edge research into production-ready solutions analyze large-scale datasets and develop new approaches for benchmarking and improving AI quality. This is a unique opportunity to solve challenging technical problems at the intersection of machine learning software engineering and data while helping shape the future of AI at Apple.

Design develop and deploy evaluation systems and scalable software that improve the quality of AI robust infrastructure to support model training benchmarking and large-scale model performance identify quality issues and develop innovative techniques to measure and improve AI with machine learning researchers software engineers and product teams to translate research into production-ready technical excellence by contributing to architecture code reviews experimentation and engineering best practices.n

MS or PhD in Computer Science Machine Learning Electrical Engineering or a related technical field or equivalent practical experience.n12 years of industry experience or equivalent academic or internship experience developing machine learning or AI in Python and familiarity with C or another object-oriented programming with one or more machine learning frameworks such as PyTorch TensorFlow or of machine learning fundamentals including supervised learning model evaluation and statistical working with data processing model training or experimentation through coursework research internships or industry analytical problem-solving and communication skills with the ability to collaborate effectively in a team environment.

Experience with large language models (LLMs) multimodal AI or generative AI through internships research or personal building software or machine learning projects using modern engineering practices (Git testing CI/CD).nFamiliarity with distributed computing cloud platforms or large-scale data open-source contributions or participation in machine learning or PhD specializing in Machine Learning Artificial Intelligence or a related field.

Required Experience:

IC

Join the team building the evaluation systems that enable Apples next generation of AI experiences. As a Machine Learning Engineer you will develop scalable infrastructure intelligent evaluators and data-driven methodologies that measure and improve the quality of large language models and multimoda...

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Ask Siri to name the most successful company in the world and it might respond: Apple. And it's not just out of familial pride. Apple consistently ranks highly in profit, revenue, market capitalization, and consumer cachet. In 2018, the company became the first reach a trillion dollar ... View more

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