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Senior Machine Learning Engineer

Apple· Machine Learning and AI
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BeijingFull-timePosted 2w ago

About the role

Join the Siri team at Apple to build and advance on-device and server-side ML models and adapters for Siri and Apple Intelligence, focusing on on-device LLMs, efficiency, and scalable production deployment across Apple devices.

Responsibilities

  • Model Training & Adaptation: Design, train, and fine-tune high-performance on-device LLMs and modular adapter architectures (e.g., LoRA, rank scaling, routing) for specific Siri and Apple Intelligence tasks.
  • Context & Efficiency Optimization: Innovate and implement techniques for prompt/context compression, KV cache optimization, tool-schema compaction, and efficient token representations to fit strict context-window limits.
  • Evaluation & Metric Hillclimbing: Build and maintain rigorous offline and online evaluation pipelines; design benchmarks for agentic tool use, goal completion, and multi-turn conversational accuracy; conduct deep failure analysis to drive continuous model hillclimbing.
  • Production Deployment & Stability: Partner with platform engineers to land models and adapters into production builds, validate performance across OTA updates, and monitor runtime behavior at scale.

Qualifications

  • 5+ years of hands-on industry experience building, training, and shipping production-grade deep learning and NLP systems at scale.
  • Deep Hands-on LLM Experience: Proven track record in training and fine-tuning Large Language Models (e.g., SFT, DPO/RLHF, PEFT/LoRA). Strong intuitive understanding of model behavior, loss dynamics, and scaling laws.
  • Advanced Engineering Skills: Exceptional coding skills in Python and proficiency with modern deep learning frameworks (PyTorch, DeepSpeed, Megatron-LM, Hugging Face, vLLM). Familiarity with high-performance C++ or Swift runtime integration is a plus.
  • Data & Evaluation Rigor: Solid experience designing domain-specific datasets, synthetic data generation pipelines, automated evaluation harnesses, and conducting systematic root-cause failure analysis.
  • Collaboration & Execution: Strong ownership, cross-functional communication, and project management skills; ability to navigate complex engineering constraints and deliver against tight product deadlines.
  • On-Device / Edge Deployment Experience: Deep understanding of edge-AI constraints and deployment pipelines (e.g., CoreML, ONNX, TensorRT-LLM, Apple Neural Engine / Metal optimization).
  • Agentic Modeling & Tool-Calling: Direct experience building agentic workflows, multi-turn reasoning, and function/tool-calling capabilities in compact/dense models (e.g., 2B–9B parameter class).
  • Proven research track record via publications in top-tier conferences (NeurIPS, ICML, ICLR, ACL, EMNLP) or demonstrated history of shipping industry-leading GenAI products.

Skills

Required:On-device LLMs and adapter technologiesPrompt/context optimizationEvaluation & benchmarkingProduction deploymentEdge deploymentAgentic modeling & tool-calling
Nice to have:Publications / research

Benefits

  • Equal opportunity employer with commitment to inclusion and diversity.
  • Reasonable accommodation for applicants with disabilities.
  • Accessibility-focused culture, benefits, and digital tools.
Last updated Oct 16, 2026. Interested? Apply on the company site.

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