Sr. Software Development Engineer (Applied ML) Hardware Product Design

Were looking for a senior AI/ML engineer to do applied ML at the intersection of 3D geometry manufacturing process and the tacit expertise of the people who run that process. Youll join the Data Science and Visualization (DataViz) team in Hardware Engineering at Apple working day-to-day in close partnership with Apples Advanced Development Lab (ADL) to bring machine learning into the heart of their machining and prototyping of this work sits at the intersection of two things: CAD files that describe the parts ADL manufactures and the subject matter expertise required to generate functional and beautiful parts. Your job will be to integrate ML and AI capabilities into the manual and routine parts of the process and implement those solutions to a standard machinists can genuinely trust so they can focus on the aspects that truly require their expertise.

As a senior member of this team you will design build and own ML systems end-to-end for ADLs machining design-for-manufacturing and related engineering workflows including the architectural calls about which approach fits a given problem and when to retire one that isnt scaling. Youll work directly with the people running those workflows: understanding their constraints building tools they trust and iterating with tight feedback loops. Youll choose the right tool for each job (classical statistics classical ML deep learning generative AI or pure algorithmic approaches) and make sure others understand your DataViz team is small. Youll be the senior ML IC partnering with data scientists and visualization engineers on our side with engineers and machinists on the ADL side and with a partner engineering team that contributes to the broader system. Expect real autonomy on the architectural calls and real accountability for whether the systems you ship still work six months dont expect any one candidate to bring every qualification below. What we care about most is the kind of thinking you bring to hard problems: clarity about what you do and dont know and the patience to work through ambiguity (and change your mind when the evidence asks you to). If that resonates wed love to hear from you.

Own ML systems end-to-end (problem framing through deployment) for ADLs machining design-for-manufacturing and related engineering multi-component AI workflows: how models agents and rule-based components compose where boundaries should sit and how the pieces stay debuggable as the system with 3D geometric data (CAD files) including reasoning about geometry as a first-class input to models rather than a side and apply the right modeling approach per evaluation and monitoring strategies that survive contact with messy real-world data including offline benchmarks automated checks and human-in-the-loop trade-offs system behavior and limitations clearly to technical and non-technical audiences.

Bachelors 7 YOE Masters 5 YOE or PhD 2 YOE (or equivalent professional experience) in CS Math Statistics Physics Engineering Robotics or a similar analytical field with the bulk of those years building ML systems in production or applied Python skills and fluency with the standard ML stack. Practical fluency with using and evaluating modern foundation models (LLMs VLMs) in production matters more than depth in any one training -lifecycle ML experience covering problem framing data work training evaluation and iteration with real users including the judgment to know when ML isnt the right tool at -on experience designing or extending agentic AI systems (multi-step tool-using workflows where models plan act and recover) and the evaluation frameworks that keep them reliable including ground truth that is contested expensive or partial and small labeled collaborating with non-ML domain experts: drawing out tacit expertise that may never have been written down translating their constraints into modeling decisions and communicating results back in their terms.

None of these are required but any would strengthen your application. If you bring direct experience please highlight it; if not but youre excited to grow into them wed still encourage you to with 3D geometric data formats (STEP mesh BRep) or 3D libraries such as OpenCASCADE Trimesh or applied in engineering manufacturing machining or other physical-world domains where models interact with hardware that has real-world reliable user-facing features or workflows backed by LLMs VLMs or other GenAI models particularly where visual or geometric inputs depth relevant to manufacturing software: computational geometry graph algorithms constraint satisfaction tool-path generation or CAD/CAM internals.