Machine Learning Systems Engineer

The Siri organization is looking for passionate Machine Learning Systems Engineers to join us in developing and shipping state-of-the-art generative AI technology to advance Siri and Apple Intelligence for customers. Siri is being elevated by the huge opportunities that AI brings. The organization is responsible for training on-device & cloud models, evaluating various approaches, pushing the envelope with the latest generative AI research developments, and ultimately delivering product critical models that power Siri and Apple Intelligence experiences. These models ship across a wide range of products, including iPhone, Mac, Apple Watch and more, enabling millions of people around the world to get things done every day. Our team provides an opportunity to be part of an incredible research and engineering organization. By joining the team, you will work with highly talented machine learning researchers and engineers, and work on meaningful, challenging and novel problems.

As a Machine Learning Systems Engineer, you will work closely with Siri modeling teams and other cross-functional teams to optimize model training and inference. You will be working across the ML stack, finding opportunities to make models performant, train quicker, and run faster on custom Apple Silicon. You will be joining a team that spans data, modeling, evaluation, deployment and working with engineers across ML infrastructure, inference, and framework teams. You will write production-level code to train and deploy models that will impact customers and enrich their lives. You are an ideal candidate if you:

• Are not afraid of CUDA OOM or NCCL errors
• Can dig deep into an ML library to understand how tiny details impact the model
• Can understand complex ML systems that include data, training pipeline, export, and inference engine

• Experience in model lifecycle of training, evaluation, and deployment of models
• Strong understanding of Machine Learning (ML) model architectures (e.g. Transformers, CNN) and ML training loop
• Strong proficiency in Python and ML framework such as PyTorch
• Bachelor's degree in Computer Science, Engineering, or related discipline, or equivalent industry/project experience

• Collaborative with experience working in large inter-teams projects
• Expertise in ML and LLM optimization such as quantization, KV Cache, Speculative Decoding
• Familiarity with ML training methodologies such as FSDP, DDP, and other parallelism
• Experience in an LLM training/eval library such as HuggingFace transformers, lm evaluation harness, Megatron-LM.
• Experience in optimizing LLM models and deploying LLM models
• Proficiency in a compiled programming language (e.g. Swift, C/C++, Java)

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 $181,100 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location.

Employees also have the opportunity to become a shareholder through participation in discretionary employee stock programs. Employees are eligible for discretionary restricted stock unit awards, and can purchase stock at a discount if voluntarily participating in the 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, 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 Benefits.

Note: benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote 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