Senior Machine Learning Finance Jobs in Austin, TX

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 and global 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 require you to be collaborative by learning intra-team and business process in order to build infrastructure and services to enable an effective Machine Learning practice. You will help lead the charge by developing a strong ML Ops process in a dynamic Finance environment where you will deal with unique challenges specific to Finance organizations, such as SOX and regulatory compliance. Your ability to instill and proliferate strong software engineering practices into team data science and machine learning processes will be critical.","responsibilities":"Build and operationalize AI solutions in Apple Finance
Partner with teammates and share expertise across teams
Explain technical concepts to non-technical audiences
Collaborate effectively with cross-functional teams
Instill strong engineering practices into team machine learning processes
Rapidly and reliably deliver value to the Finance organization
Preferred Qualifications
Graduate degree (computer science, data science, math, quantitative finance, or similar discipline) with five years experience
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
Experience with front end (.js experience)
Minimum Qualifications
Undergraduate degree (computer science, data science, finance, economics, accounting, or related business discipline) with seven years demonstrated experience
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 DRY principles, modularity, and testing standards, with version control, code review.
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 cloud platforms (AWS, GCP, Azure)
Ability to explain technical details to non-technical audiences
Understands and advocates version control, test driven development and strong CI/CD process
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.