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Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
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As the Finance Machine Learning Engineer - Tech Lead, you will serve as the technical lead for product cost within a multi-discipline engineering pod supporting the Operations Finance organization. You will oversee solution design and engineers while collaborating with data and software engineers to build data foundations, services, and platforms that automate decisions and deliver insights. Your primary responsibility involves operationalizing AI solutions by bridging the gap between prototype and production while instilling strong engineering practices into machine learning processes. To succeed, you must utilize Python, SQL, and big data technologies to build scalable pipelines and data models. You will apply ML algorithms for regression, classification, and anomaly detection, while building generative AI and agentic solutions using MLOps/LLMOps frameworks across cloud platforms like AWS, GCP, or Azure.
Serve as the technical lead overseeing solution design and engineering efforts for product cost.
Operationalize AI solutions by bridging the gap between initial prototypes and production environments.
Implement MLOps and LLMOps practices including CI/CD, drift monitoring, and cloud platform integration.
Build scalable data models and pipelines using SQL and big data technologies.
Develop machine learning models for regression, classification, anomaly detection, and generative AI solutions.
Establish strong engineering practices and standards for the team's machine learning processes.
Translate complex technical concepts into understandable information for non-technical stakeholders.
Build data foundations and platforms to automate decisions for the Finance organization.
What we're looking for
At least 8 years of experience in an engineering role.
At least one year of experience leading engineers, collaborating cross-functionally, and translating technical concepts for diverse audiences.
Experience building data models and scalable pipelines using SQL and big data technologies with expertise in data ops best practices.
Experience developing in Python following DRY principles, modularity, testing standards, version control, and code reviews.
Experience with front end (.js) development.
Experience applying ML algorithms for regression, classification, and anomaly detection; building generative AI/agentic solutions; and implementing MLOps/LLMOps on cloud platforms.
Graduate degree in computer science, data science, math, quantitative finance, or a similar discipline.
Undergraduate degree in computer science, data science, finance, economics, accounting, or a related business discipline.
Previous experience working in corporate finance, accounting, or supply chain (preferred).
Understanding of financial statements, P&L impact, accounting principles, SOX/tax compliance, and month-end close processes (preferred).