Finance Digital Transformation - Senior Machine Learning Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified last checked Jun 13, 2026

Salary

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

102% above the BLS national median for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

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

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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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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