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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - **Company:** Apple Inc. - **Location:** Seattle, WA, United States - **Experience:** Expert - **Salary:** $175,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Big Data, Cloud Computing, Program Optimization, Computer Programming, Computer Engineering, System Configuration, Data Cleansing, Extract Transform Load (ETL), Data Security, Data Systems, Distributed Data Store, Distributed Systems, Python (Programming Language), Machine Learning, Network Control, Tensorflow, Management of Software Versions, Privacy Controls, Parquet, Data Ingestion, Pytorch, Retrieval-Augmented Generation, Apache Spark, Generative AI, Build Management, AI Platforms, Core Data, Kubernetes, Information Technology, Data Lineage, Machine Learning Operations, Stable Diffusion, Data Pipelines, Docker - **Published:** September 11, 2026 - **Apply:** https://www.jobmonkeyjobs.com/career/28010242/Senior-Machine-Learning-Engineer-Washington-Seattle-7413 ## About the Role Strong foundation in machine learning, with hands-on experience across the end-to-end ML workflow - including data preparation, pipeline development, experimentation, evaluation, and deployment Expertise in building and running large scale distributed systems Familiarity with modern generative techniques (e.g. transformers, diffusion, retrieval-augmented generation) Proven experience building and delivering data and machine learning infrastructure in real-world production environments Familiarity with fine-tuning workflows, model optimization, and preparing models for scalable inference Familiarity with generative AI and its applications in accelerating and enhancing machine learning workflows Experience configuring, deploying and troubleshooting large scale production environments Experience in designing, building, and maintaining scalable, highly available systems that prioritize ease of use Extensive programming experience in Java, Python or Go Strong collaboration and communication (verbal and written) skills Comfortable navigating ambiguity and evolving technical landscapes, especially in fast-moving areas B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience Preferred Qualifications Experience in any of the below is preferred: Proficiency with one or more modern ML frameworks (PyTorch, JAX, or TensorFlow), particularly the data loading and dataset access layer Columnar and lakehouse formats: Parquet, Iceberg, Delta, or Lance Distributed data loading frameworks for ML: Ray Data, NVIDIA DALI, WebDataset, or Mosaic StreamingDataset Performance engineering for I/O-bound workloads - Arrow, zero-copy, memory mapping, async I/O High-throughput object storage access patterns at GPU scale Data lineage and governance systems (DataHub, OpenLineage, Unity Catalog, or equivalent) Contributions to or operational experience with Spark, Daft, Polars, or DuckDB internals Containerization and orchestration technologies (Docker, Kubernetes) ## Description The Apple Cloud AI Platform team enables Apple's next generation of intelligent products by giving Apple's ML engineers and researchers the data systems and large-scale compute they need to build and ship models at Apple's bar for quality and privacy. Responsibilities As a member of the Apple Cloud AI Platform team, your responsibilities will include: Design and build the platform behind Apple's largest model builds - ingestion, immutable versioning, lineage, and governance across structured, unstructured, and multimodal data at petabyte scale, so every model run is reproducible from a versioned dataset Develop and evolve Python SDKs and core data libraries that ML engineers depend on to access, transform, and load model-ready datasets across every stage of model development Build high-throughput data access and loading primitives that feed Apple's largest GPU fleets, keeping workloads compute-bound rather than I/O-bound Build and operate distributed data pipelines spanning Spark, Daft, and Rust-based systems for ingestion, transformation, and large-scale data preparation Optimize platform components for tight integration with leading ML frameworks - PyTorch, JAX, and TensorFlow - so dataset access is a first-class concern in the model development loop Partner with research and product teams to onboard new data sources, and enable rapid iteration on datasets powering GenAI workloads Ensure governance is a first-class platform capability: Legal Terms of Use enforcement, privacy controls, and end-to-end data lineage on every dataset version Drive efficiency, reliability, and automation across the data plane and control plane that power Apple's ML fleet Continuously evolve platform capabilities to support next-generation workloads, including foundation models, multimodal data, and retrieval-augmented systems Diagnose, fix, and automate away complex issues across the stack - from ingestion pipelines to dataset APIs to ML framework integrations - to maximize uptime and throughput ## Related Videos - [Parquet, Delta, Iceberg & Ducklake - An introduction for developers](https://www.wearedevelopers.com/videos/100075-parquet-delta-iceberg-ducklake-an-introduction-for-developers) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [OLAP for AI Applications and why you should care](https://www.wearedevelopers.com/videos/100212-olap-for-ai-applications-and-why-you-should-care) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)