Machine Learning Engineer, Computer Vision - Strategic Data Solutions

Apple; Austin (TX); On-site; USD 90,000 - 150,000; Full time

Job summary

An innovative firm is seeking a passionate Machine Learning Engineer to join their Strategic Data Solutions team. This role focuses on developing cutting-edge computer vision solutions to combat fraud while enhancing customer experiences. You will engage in building end-to-end analytical solutions, working closely with data scientists and engineers to deploy models in real-time decision-making platforms. The position offers the opportunity to tackle exciting challenges in a dynamic environment, where your contributions will directly impact the company's success. If you thrive in a fast-paced setting and are eager to make a difference, this role is perfect for you.

Qualifications

• M.S. or Ph.D. in a technical field with experience in computer vision.
• Practical experience with computer vision methods or deep learning models.

Responsibilities

• Translate business needs into technical solutions and deploy analytics.
• Analyze data to identify patterns of fraudulent behavior.

Skills

Computer Vision

Machine Learning

Data Analysis

Communication Skills

Programming in Python

Problem Solving

Education

M.S. or Ph.D. in Computer Science

Equivalent 2 years of proven experience

Tools

SQL

ConvNet Models

Job description

Machine Learning Engineer, Computer Vision - Strategic Data Solutions

Austin, Texas, United States Machine Learning and AI

Summary

Posted: Mar 01, 2025

Weekly Hours: 40

Role Number: 200590752

Apple's Strategic Data Solutions (SDS) team is looking for a hard-working individual who is passionate about implementing and operating analytical solutions that have direct, measurable impact to Apple and its customers. As a Computer Vision MLE on the SDS team, you will build end-to-end solutions for preventing fraud, waste, and abuse while safeguarding the customer experience. The day-to-day work consists of identifying new fraud leads by connecting different data sources together; working with a team of annotators to curate a labeled image dataset; building computer vision pipelines or training ConvNet models; deploying the analytic product into a real-time decisioning platform; and ensuring ongoing operational excellence by monitoring and making operational adjustments. The adversarial nature of fraud and the large scale of the business make for exciting challenges. On this team, we apply a variety of data science approaches towards the mitigation of fraud!

Description

1. Translate business needs into technical solutions. Work with program managers, data scientists, and business partners to incorporate analytic solutions into business processes.
2. Explore tabular and image datasets to identify patterns of fraudulent behavior.
3. Move quickly to combat adversarial attacks by developing and deploying updated models.
4. Analyze data and communicate findings to audiences with various technical backgrounds (e.g., engineers, program managers, and executives).
5. Coordinate with engineers and system administrators to deploy analytics to a real-time decisioning platform.
6. Identify and implement new data science tools to complement the existing capabilities of the team.

Minimum Qualifications

• M.S. or Ph.D. in Computer Science, Machine Learning, Engineering, or other technical field with experience in computer vision or machine learning or equivalent 2 years of proven experience.
• Practical experience and theoretical understanding of computer vision methods (e.g., local feature matching) or deep learning models (e.g., CNN).

Preferred Qualifications

• Excellent communication skills, including the ability to distill complex ideas into concise business-focused messages.
• Ability to code and debug in a general programming language such as Python or Java.
• Ability to extract business insights from data and identify the stories behind the patterns.
• Ability to navigate complex systems spanning toolchains and teams.
• Experience with a data query language (e.g., SQL).