Finance Digital Transformation - Senior Machine Learning Engineer

Benefits

Parental leave

Not verified

Non-birth-parent leave

Not verified

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

Not verified

Childcare support

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Learning budget

Not verified

Verification

Not verified checked Jun 13, 2026

Salary

$181K-$272K From the posting source checked Jun 20, 2026

401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

94% above the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

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Company

Equity

Offered Verified - SEC 10-K source checked Jun 20, 2026

Application

Cover letter

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Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Finance Digital Transformation - Senior Machine Learning Engineer Austin, United States of America 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 senior machine learning engineer in Finance, you'll play an integral and global role in building the platform, data foundations, and services used for transforming Finance's organization. You'll learn intra-team and business process to build infrastructure and services enabling an effective Machine Learning practice. You will help lead the charge by developing robust AIML-driven processes and extending scalable platforms to optimize financial operations in a dynamic environment. You will tackle unique challenges specific to Finance organizations - including SOX compliance, regulatory requirements, cost variance analysis, margin analysis, and scenario modeling - while driving automation and efficiency across end-to-end finance workflows. Your ability to instill and proliferate strong software engineering practices into team data science and machine learning processes will be critical. Extend and improve existing platform capabilities for generative AI, agentic workflows, and machine learning inference use cases Drive SLO definition, close observability gaps, and strengthen operational posture across ML stack Develop and deploy frameworks for agentic AI, including evaluation and knowledge management Harden CI/CD and MLOps practices with testing, drift monitoring, and

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