Finance Machine Learning Engineer - Tech Lead

Apple Inc.; Austin (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

Apple Inc. in Austin, Texas is seeking a Finance Machine Learning Engineer - Tech Lead to own the design and delivery of ML-powered finance insights and automated decisions.

You will lead an interdisciplinary team, translate complex concepts for business partners, and drive the operationalization of AI solutions from prototype to production while ensuring robust engineering practices. This role emphasizes building data foundations, scalable pipelines, and close collaboration with product, data,

Qualifications

• At least 8 years in an engineering role.
• At least 1 year leading engineers and cross-functional collaboration, translating technical concepts for diverse audiences and converting ideas into solutions with strong process and data understanding
• Experience building data models and scalable pipelines using SQL and big data tech, with expertise in data ops best practices
• Experience developing in Python while following and advocating for DRY principles, modularity, testing standards, version control, and code reviews. Experience with front end (.js experience)
• Experience applying ML algorithms for regression, classification, and anomaly detection; build generative AI and agentic solutions; implement MLOps/LLMOps including CI/CD, drift monitoring, and familiarity working with cloud platforms (AWS, GCP, Azure)
• Undergraduate degree (computer science, data science, finance, economics, accounting, or related business discipline)

Responsibilities

• Technical lead overseeing solution design and engineers
• ,
• Partner with teammates and share expertise across teams
• ,
• Explain technical concepts to non-technical audiences
• ,
• Collaborate effectively with cross-functional teams
• ,
• Operationalize AI solutions, bridging the gap between prototype and production
• ,
• Instill strong engineering practices into team machine learning processes
• ,
• Rapidly and reliably deliver value to the Finance organization

Skills

Leadership

SQL

Python

Data modeling

Big data pipelines

ML algorithms

MLOps / CI/CD

Cloud platforms (AWS, GCP, Azure)

Front-end JavaScript

Education

Undergraduate degree in computer science, data science, finance, economics, accounting, or related business discipline

Tools

JavaScript

Job description

Finance Machine Learning Engineer - Tech Lead

Austin, Texas, United States Corporate Functions

Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and curiosity to your job and there's no telling what you could accomplish. Do you love thinking analytically? Just as our customers find value in Apple products, the Finance group finds value for both Apple and its shareholders. As a machine learning engineer in Finance, you’ll play an integral role in building the data foundations, services, and platforms used for delivering insights and automating decisions for Apple’s Finance organization.

Description

This role will be the technical lead for product cost, supporting our Operations Finance organization. You will work as part of a multi-discipline engineering pod with data and software engineers, product managers and program managers. Your ability to learn business processes and instill strong engineering practices into team machine learning processes will be critical. A key part of your role will be to operationalize AI solutions, bridging the gap between prototype and production to rapidly and reliably deliver value to the Finance organization.

Responsibilities

• Technical lead overseeing solution design and engineers
• Partner with teammates and share expertise across teams
• Explain technical concepts to non-technical audiences
• Collaborate effectively with cross-functional teams
• Operationalize AI solutions, bridging the gap between prototype and production
• Instill strong engineering practices into team machine learning processes
• Rapidly and reliably deliver value to the Finance organization

Minimum Qualifications

• At least 8 years experience in an engineering role
• At least one year experience effectively leading engineers and collaborating cross-functionally, translating technical concepts for diverse audiences and converting ideas into solutions with strong process and data understanding
• Experience building data models and scalable pipelines using SQL and big data technologies, with expertise in data ops best practices
• Experience developing in Python while following and advocating for DRY principles, modularity, testing standards, version control, and code reviews. Experience with front end (.js experience)
• Experience applying ML algorithms for regression, classification, and anomaly detection; build generative AI and agentic solutions; implement MLOps/LLMOps including CI/CD, drift monitoring, and familiarity working with cloud platforms (AWS, GCP, Azure)
• Undergraduate degree (computer science, data science, finance, economics, accounting, or related business discipline)

Preferred Qualifications

• Previous experience working in a corporate finance, accounting, or supply chain organization
• Understanding of or ability to learn financial statements, P&L impact, high level accounting principles, SOX and tax compliance and month-end close process

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

Learn about accessibility in Apple’s workplace

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Apple accepts applications to this posting on an ongoing basis.