Full Job Description
We're 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. You'll join the Data Science and Visualization (DataViz) team in Hardware Engineering at Apple, working day-to-day in close partnership with Apple's Advanced Development Lab (ADL) to bring machine learning into the heart of their machining and prototyping workflows.\nMuch of this work sits at the intersection of two things: CAD files that describe the parts ADL manufactures, and process knowledge that lives in the heads of engineers and machinists rather than in any document. Your job is to work with partners to surface that tacit expertise, encode it into tools they can rely on, and keep those tools honest with disciplined evaluation.\n\nAs a senior member of this team, you will design, build, and own ML systems end-to-end for ADL's 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 isn't scaling. You'll work directly with the people running those workflows: understanding their constraints, building tools they trust, and iterating with tight feedback loops. You'll 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 logic.
The DataViz team is small. You'll 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 later.
We don't 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 don't know, and the patience to work through ambiguity (and change your mind when the evidence asks you to). If that resonates, we'd love to hear from you.
Bachelor's + 7 YOE, Master's + 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 settings.
Strong 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 framework.
Full-lifecycle ML experience covering problem framing, data work, training, evaluation, and iteration with real users, including the judgment to know when ML isn't the right tool at all.
Hands-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 datasets.
Comfort 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.
Familiarity with 3D geometric data formats (STEP, mesh, BRep) or 3D libraries such as OpenCASCADE, Trimesh, or PyTorch3D.
ML applied in engineering, manufacturing, machining, or other physical-world domains where models interact with hardware that has real-world constraints.
Building reliable, user-facing features or workflows backed by LLMs, VLMs, or other GenAI models, particularly where visual or geometric inputs matter.
Algorithmic depth relevant to manufacturing software: computational geometry, graph algorithms, constraint satisfaction, tool-path generation, or CAD/CAM internals.
