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# Senior Machine Learning Engineer, Apple Cloud AI

- **Company:** Apple Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $142,300.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Big Data, Encodings, Computer Programming, Data Cleansing, Distributed Computing Environment, Distributed Systems, Amazon DynamoDB, Python (Programming Language), Machine Learning, Performance Tuning, Redis, Azure Machine Learning, Reinforcement Learning, Feature Engineering, Normalized Discounted Cumulative Gain, Large Language Models, Apache Spark, Caching, Discretization, AI Platforms, Kubernetes, Information Technology, Apache Flink, Cassandra, Ray Serve, Machine Learning Operations, TensorRT, Hardware Infrastructure, VLLM, Model Inference
- **Published:** October 4, 2026
- **Apply:** https://www.jobmonkeyjobs.com/career/28078937/Senior-Machine-Learning-Engineer-Apple-Cloud-Ai-Washington-Seattle-7413

## About the Role

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

## 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.

Responsibilities

As a member of the team, your responsibilities will include:

Design, build, and optimize large-scale ML platform services used by teams across Apple

Build the embedding and retrieval path end to end - fine-tuning encoder models, encoding corpora at scale, building and serving vector indexes, and evaluating retrieval quality so improvements are measurable rather than asserted

Build and operate the feature store teams use for training and serving, keeping both paths consistent off a single feature definition

Develop optimization capabilities that reduce cost and improve quality across ML workloads - including model routing, caching, serving configuration, inference optimization, and training efficiency

Build managed, self-service experiences so customers can go from data to production AI with minimal friction

Build managed training - supervised fine-tuning, reinforcement learning and distillation - so teams can customize models without running their own training infrastructure

Build governance and compliance capabilities - lineage, policy enforcement, cost observability, and access control

Partner with customer teams across Apple to understand their ML workloads and deliver production solutions