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Apple Inc. is seeking an on-device ML engineer to advance Calendar’s on-device intelligence. You will drive modeling and evaluation from framing to launch, collaborating with senior systems engineers and design teams.
You’ll build models, evaluate signals, define metrics, and iterate with cross-functional partners to ship private, efficient features across Apple platforms. You will work across teams to ensure privacy, performance, and reliability, contributing to a calendar experience used by
Apple has changed how people live, connect, and work, and the Calendar team wants to rethink what a calendar can do for the people using it. A calendar knows when someone is busy. It knows very little about which of those commitments matter, which ones could move, or when would be a good time for something new. We're looking for an engineer to help answer those questions, on device and privately, for billions of people. The right person has strong machine learning foundations and is just as comfortable writing the software that ships a model as building the model itself. You'll help take ideas from a single sentence to a shipped feature, and you'll bring the evaluation that tells us whether it works.
In this role you'll drive the modeling and evaluation behind intelligence features in Calendar, from the first framing through launch, as part of a very collaborative team. A request will often start as one line, and you'll work with the team to turn it into a problem we can solve: what signal exists, what a good answer looks like, which approach fits, and how we'll know it's right. You'll design the evaluation before you build the model. That means the metrics, the eval sets, the baselines a model has to beat, and human review for the places where a metric can't tell you enough.
You won't always have good data, so part of the job is finding it and getting something useful out of it when there isn't much. You'll need to get real statistical strength out of priors, shared structure, and self-supervision. You'll also work with a range of large language models, on device and on server, deciding where a model call earns its cost, where a smaller model trained on a larger model's judgments does better, and where a well-tuned heuristic beats both.
You'll join a small team of senior systems engineers who ship this work across Apple's platforms, several of them with deep backgrounds in ML data engineering. There's no hard line between roles, so you'll write production code alongside them in whatever part of the stack the problem needs. Calendar also builds on signals and models from other ML teams across Apple, and a lot of this role is working with them: building on what they have, agreeing on shared interfaces, and recognizing when we need our own. You'll work closely with design and product as well. We need someone who writes clearly, can explain a model's tradeoffs to engineers who aren't ML specialists, and is comfortable driving a problem forward before the path is defined.
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this rol