Senior Machine Learning Engineer

Join a team at the forefront of ML infrastructure and generative AI where data and model workflows come together to enable the next generation of intelligent experiences on Apple products and services. We build robust systems that connect scalable data pipelines with advanced ML workflows accelerating the development of real-world AI applications. Our work spans the full ML lifecycle from experimentation to deployment and youll play a key role in shaping how AI models are built optimized and scaled. We develop a platform for ML data and features that powers advanced GenAI applications. This includes embeddings (generation evaluation ANN search multimodal support) AI Ops efficient inference and a modern feature platform designed to streamline experimentation and drive innovation. Were looking for engineers and researchers passionate about generative models data-centric ML and intelligent systems across diverse real-world use cases. With the autonomy to experiment the scale to make an impact and the support to take ideas from prototype to production youll work alongside a world-class team to build intelligent flexible systems that make ML development faster more reliable and more creative. n

The Apple Cloud AI Platform team enables Apples next generation of intelligent products by giving Apples ML engineers and researchers the data systems and large-scale compute they need to build and ship models at Apples bar for quality and privacy.

As a member of the Apple Cloud AI Platform team your responsibilities will include:nDesign and build the platform behind Apples 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 datasetnDevelop 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 developmentnBuild high-throughput data access and loading primitives that feed Apples largest GPU fleets keeping workloads compute-bound rather than I/O-boundnBuild and operate distributed data pipelines spanning Spark Daft and Rust-based systems for ingestion transformation and large-scale data preparationnOptimize 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 loopnPartner with research and product teams to onboard new data sources and enable rapid iteration on datasets powering GenAI workloadsnEnsure governance is a first-class platform capability: Legal Terms of Use enforcement privacy controls and end-to-end data lineage on every dataset versionnDrive efficiency reliability and automation across the data plane and control plane that power Apples ML fleetnContinuously evolve platform capabilities to support next-generation workloads including foundation models multimodal data and retrieval-augmented systemsnDiagnose fix and automate away complex issues across the stack from ingestion pipelines to dataset APIs to ML framework integrations to maximize uptime and throughputnn

Strong foundation in machine learning with hands-on experience across the end-to-end ML workflow - including data preparation pipeline development experimentation evaluation and deploymentnExpertise in building and running large scale distributed systemsnFamiliarity with modern generative techniques (e.g. transformers diffusion retrieval-augmented generation)nProven experience building and delivering data and machine learning infrastructure in real-world production environmentsnFamiliarity with fine-tuning workflows model optimization and preparing models for scalable inferencenFamiliarity with generative AI and its applications in accelerating and enhancing machine learning workflowsnExperience configuring deploying and troubleshooting large scale production environmentsnExperience in designing building and maintaining scalable highly available systems that prioritize ease of usenExtensive programming experience in Java Python or GonStrong collaboration and communication (verbal and written) skillsnComfortable navigating ambiguity and evolving technical landscapes especially in fast-moving areasnB.S. M.S. or Ph.D. in Computer Science Computer Engineering or equivalent practical experiencen

Experience in any of the below is preferred: nProficiency with one or more modern ML frameworks (PyTorch JAX or TensorFlow) particularly the data loading and dataset access layer nColumnar and lakehouse formats: Parquet Iceberg Delta or Lance nDistributed data loading frameworks for ML: Ray Data NVIDIA DALI WebDataset or Mosaic StreamingDataset nPerformance engineering for I/O-bound workloads Arrow zero-copy memory mapping async I/O nHigh-throughput object storage access patterns at GPU scale nData lineage and governance systems (DataHub OpenLineage Unity Catalog or equivalent) nContributions to or operational experience with Spark Daft Polars or DuckDB internals nContainerization and orchestration technologies (Docker Kubernetes)n