Machine Learning and Software Engineer

As a member of our fast-paced group, you will have the unique and rewarding opportunity to shape upcoming products from Apple. We are looking for a highly motivated and versatile Machine Learning and Software Engineer with a broad skill set. The ideal candidate combines strong engineering fundamentals with a keen product sense, and is a quick learner who is comfortable jumping into entirely new codebases and architectures. You must be an effective communicator and an adaptable problem solver, willing to take on whatever represents the optimal solution for the product, whether that means training machine learning models, writing robust software heuristics, or iterating on prompt engineering., Bachelor’s in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

6+ years experience in shipping Search and ML systems

Strong programming skills in Python, Go or similar languages, with a solid foundation in software design, data structures, and algorithms.

Experience building scalable data pipelines and backend systems, coupled with the ability to quickly navigate and contribute to large, complex, and unfamiliar codebases.

Proficiency in data science methodologies, including A/B testing, statistical hypothesis testing and defining offline and online evaluation metrics for ML systems.

Experience with prompt engineering

Strong product sense with the ability to translate ambiguous product requirements into concrete engineering and modeling tasks.

Excellent communication skills, with the ability to back up decisions with rigorous data analysis and present findings clearly to cross-functional partners.

Preferred Qualifications

Master’s/Ph.D in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

4+ years experience in shipping Search and ML systems

Hands-on experience in Information Retrieval, Search Ranking, or Natural Language Processing (NLP).

Demonstrated experience designing, building, or working with Retrieval-Augmented Generation (RAG) architectures and applications.

Familiarity with techniques for optimizing the inference performance, latency, and resource efficiency of LLMs and large machine learning systems in production environments (e.g., model quantization, caching, distributed inference).

Experience with training and deploying machine learning models