Senior Machine Learning Engineer, Apple Cloud AI

Apple is a place where extraordinary people gather to do their best work.

Together we build products and experiences people love. The Apple Services

Engineering (ASE) organization builds and operates the systems and

infrastructure that power Apple's services at scale. The Apple AI platform

within ASE enables teams across Apple to build, train, optimize, and deploy AI

systems at scale. Our team builds the optimization and intelligence layer for

frontier AI, making frontier class of models work better, cheaper, and faster

through managed, serverless capabilities that span the full AI lifecycle: data

and feature engineering, embeddings and retrieval, model training and

fine-tuning, inference optimization and routing, prompt optimization,

evaluation, and governance.

DESCRIPTION

We are looking for an ML engineer who is excited about building managed platform

services at the intersection of ML, distributed systems, and production

engineering.

MINIMUM QUALIFICATIONS

3+ years of experience building production ML systems or ML infrastructure

Strong programming skills in Python and/or Rust/Java Understanding of end-to-end

machine learning workflows - from data preparation through training, evaluation,

and deployment Experience with distributed systems and large-scale data

processing Experience with model serving, inference optimization, or ML pipeline

engineering Experience building APIs and services that other engineers consume

Strong collaboration and communication skills Comfortable navigating ambiguity

in fast-moving areas BS, MS, or PhD in Computer Science or equivalent practical

experience

PREFERRED QUALIFICATIONS

Experience with LLM inference optimization (batching, quantization, KV caching,

tensor parallelism) Experience with model serving frameworks (vLLM, TensorRT,

Ray Serve, or similar) Experience with embedding models and retrieval systems -

fine-tuning encoders on graded or contrastive objectives, pooling strategies,

dimensionality reduction for serving cost, vector databases, and retrieval

evaluation (NDCG, recall, graded relevance) Experience with fine-tuning and

alignment workflows (SFT, DPO, LoRA, RLHF, RLVR, GRPO, reward modeling)

Experience with feature engineering and feature serving platforms (e.g. Feast,

Tecton, Hopsworks), distributed data processing frameworks (e.g. Spark, Flink,

Ray), offline stores (e.g. Iceberg, Delta, Lance), and online stores (e.g.

Redis, Cassandra, DynamoDB) Experience with Ray, Kubernetes, and cloud GPU

infrastructure (AWS, GCP) Experience with ML governance, lineage, or compliance

systems