Sr. Software Development Engineer (Applied ML)

In this pivotal role, you will take ownership of the end-to-end lifecycle of machine learning solutions, from cutting-edge research and model development to robust implementation and deployment. You will apply advanced ML techniques to optimize and innovate across critical manufacturing processes like machining and design for manufacturing (DFM). Our core mission is to craft and deploy high-quality, predictive ML models and intelligent prototypes that directly translate into superior manufacturing solutions, ensuring unparalleled quality and precision. Design, build, and own end-to-end GenAI capabilities that support both a centralized AI platform and prototyping teams, covering all aspects from prompt and tool design to agent orchestration, retrieval strategies, model selection, and system evaluation. Leverage agentic AI patterns (multi-step reasoning, tool use, planning, memory, feedback loops) to support complex workflows, while establishing guardrails for reliable and predictable behavior. Establish evaluation and monitoring strategies for GenAI-driven applications, focusing on output quality, correctness, safety, and business relevance through offline benchmarks, automated checks, and human-in-the-loop review. Design, fine-tune, and deploy Large Language Models (LLMs), with a focus on hyperparameter optimization, experimental design (DOE), and rigorous evaluation. Communicate trade-offs, system behavior, and limitations clearly to technical and non-technical stakeholders, enabling informed product and business decisions. Collaborate with Apple’s cross-functional teams to apply and deploy machine learning to industrial problems. Bachelor’s degree in Computer Science, Math, Statistics, Physics, Engineering, or a similar field. 5+ years of hands-on experience in building machine learning algorithms to solve real-world problems. Strong programming skills with proficiency in Python and GitHub. Experienced user of machine learning libraries such as scikit-learn, scipy, and Tensorflow/PyTorch. Ability to explain and present machine learning concepts and results to a broad technical audience and executives. Master’s or PhD degree in Computer Science, Math, Statistics, Physics, Engineering, or related level of experience. Practical experience leveraging LLMs or GenAI models via APIs to create reliable and user-facing features or workflows. Familiarity with common GenAI tools and frameworks, such as LangChain or similar, with the ability to learn and adapt as the ecosystem evolves. Solid understanding of foundational ML concepts, including supervised, unsupervised, and reinforcement learning. Experience applying deep learning frameworks, such as PyTorch/Torch, TensorFlow, or Keras, to real-world applications. Experience in data analytics, machine learning, and/or computer vision for manufacturing problems is a bonus.

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