Machine Learning Engineer — Trust and Safety (Account Trust) at Apple

Cupertino, California, United States -

Full Time

Start Date

Immediate

Expiry Date

30 Jun, 26

Salary

0.0

Posted On

01 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Analysis, Python, SQL, Spark, Jupyter Notebook, Collaboration, Communication, Fraud Prevention, Deep Learning, Anomaly Detection, Clustering, Classifiers, Software Development, Big Data, ETL

Industry

Computers and Electronics Manufacturing

Description

The Trust and Safety group at Apple is responsible for ensuring that users of Apple’s services and products have genuine and safe experiences. Within Trust and Safety, our team works to protect Apple’s communication apps and related platforms from spam and other abuse. The solutions we deploy help us keep Apple ecosystem safe, while mitigating constant attack by external actors. We are seeking a machine learning engineer who will strive to turn data into actionable insights that improve safe customer experience. Successful candidates will have a demonstrated history of self-directed research and investigations spanning sophisticated, interdependent systems that led to novel insights directly impacting well-defined success metrics. DESCRIPTION Success in this role is defined by your ability to: •Simplify complex systems and work with technical and non-technical stakeholders to build solutions to align for specific use cases. •Build machine learning tooling to facilitate various phases of the ML lifecycle from model training, data ETL, end-to-end model evaluation and deployment. •Deliver reusable and easy-to-use tooling to integrate with existing data and machine learning systems. •Build strong partnerships to close data gaps and mitigate attack vectors. •Identify weaknesses, propose better fraud-fighting tools, and anticipate attacker adaptations. This role requires exceptional collaboration across Data Science, Software Engineering, and Machine Learning Research. You’ll work with partner teams to develop strategic, long-term fraud prevention solutions while continuously enhancing your software engineering and machine learning expertise. MINIMUM QUALIFICATIONS Proven experience in anti-fraud (or similar) with at least two complex investigations in incomplete data environments, demonstrating initiative and measurable impact. Strong understanding of machine learning algorithms (including classifiers, clustering algorithms, and anomaly detection), especially in the context of LLMs. 3+ years of proficiency in Python, including machine learning packages like Jax/Tensorflow or PyTorch. 3+ years of experience with big data tools (SQL, Spark, Splunk, Python, Jupyter Notebook). Experience collaborating across engineering and non-engineering teams. Strong interpersonal verbal and written communication skills with the ability to work effectively across internal and external organizations and virtual teams. BS/BA or equivalent degree in computer science or similar (preferred). PREFERRED QUALIFICATIONS 5+ years experience with Python, Scala, Java, or similar, including relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch, Spark MLlib). 5+ years of industry software development experience using source control (e.g., Git). Advanced degree (MS/PhD) in a quantitative field (Computer Science, Statistics, Mathematics, Operations Research). Hands-on experience implementing machine learning solutions (classifiers, clustering, anomaly detection). Experience building scalable deep learning systems. Experience with large scale data infrastructure. Curiosity, integrity, and a passion for learning and enhancing the Apple customer experience. Excellent interpersonal, written, and verbal communication skills Curiosity, passion for learning, high personal integrity, and a dedication to improving the Apple customer experience

Responsibilities

The Machine Learning Engineer will simplify complex systems and collaborate with stakeholders to build solutions for specific use cases. They will also develop machine learning tooling for various phases of the ML lifecycle and work on fraud prevention strategies.