AIML - Machine Learning Engineer (On-Device Machine Learning), Machine Learning Platform and In[...]
4 weeks ago
At Apple, the AIML the on-device Machine Learning group is responsible for accelerating the adoption of machine learning to create extraordinary experiences for our users. We are looking for a tenured software engineer to help implement innovative (LLMs, Diffusion, GenAI, etc) machine learning models against our powerful machine learning hardware. This is a unique opportunity to work on powerful new technologies and contribute to Apple's ecosystem, with a commitment to privacy and user experience impacting millions of users worldwide. This role requires a strong engineering sense of building the right solution for a given problem, defining the right APIs, attention to performance, and work with both 1st and 3rd party clients to determine how to best run their models on Apple silicon. If all of this sounds exciting, come join us and be part of the team that is helping Machine Learning developers innovate and ship enriching experiences on Apple devices
Description
As a member of this team, the successful candidate will build features for our on-device inference stack to support new and existing customers with ever evolving machine learning models. This person will interact with first and third part clients to figure out their functional and performance requirements and work collaboratively with backend teams to map those requirements to functional execution. They will perform analysis when needed to squeeze every ounce of memory for flop from the underlying hardware, and they will drive structural improvements that can be made in the on device machine learning stack.
Minimum Qualifications
- 5+ years proven programming skills using standard ML tools such as C/C++, CUDA/Metal, PyTorch, Tensorflow
- Experience building APIs and/or core components of ML frameworks and strong attention to detail
- Capacity to iterate on ideas, work with a variety of partners from all parts of the stack — from apps to Compilation, HW Arch, and Power/Performance analysis
- Excellent problem-solving (e.g. via building forward-looking prototype systems), critical thinking, strong communication, and collaboration skills.
Preferred Qualifications
- Knowledge of existing ML Framework implementation (Jax, PyTorch, TensorFlow)
- Knowledge of code execution down to the hardware/accelerator level
- Hands-on experience working on LLVM, compiler technologies, optimization techniques like quantization and sparsity-induction is a huge plus
- M.S. or B.S. or equivalent experience in Computer Science
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 $135,400 and $250,600, 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 role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
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