Senior​/Machine Learning Engineer, ACAI Training & Inference Platform

Position: Senior / Staff Machine Learning Engineer, ACAI Training & Inference Platform
Role Number:
Team:
Software and Services

Location:

Cupertino Posted:
Summary The ACAI Training & Inference Platform team is seeking a Senior / Staff Software Engineer with strong distributed systems expertise and deep experience in AI training and inference. As part of Apple Cloud AI (ACAI) within Apple’s Data Platform (ADP), you will design and build core capabilities for large-scale model training, post-training, fine-tuning, reinforcement learning, and high-performance inference.

You will develop scalable AI execution frameworks, developer-facing APIs, and runtime optimizations that make it easier to train, adapt, and serve advanced models efficiently across Apple’s AI ecosystem. You will partner closely with AI researchers and engineers to translate evolving model architectures and workload requirements into reliable, high-performance training and inference solutions. This role is ideal for an engineer who combines deep distributed-systems expertise with hands-on knowledge of modern LLM training, post-training, and inference optimization.

Description ADP brings together the Apple Cloud Data Platform (ACDP), Apple Cloud AI (ACAI), and Apple Governance Platform (AGP) to provide foundational capabilities for data, analytics, AI, and governance at Apple scale. Within ACAI, the Training & Inference Platform team enables scalable model development and production inference across Apple’s AI ecosystem.

As a Senior / Staff Software Engineer, you will build capabilities spanning the model lifecycle, from distributed training and post-training to optimized inference and serving.

On the training side, you will develop frameworks and APIs for fine-tuning, reinforcement learning, and post-training workflows, enabling AI teams to efficiently adapt foundation models using techniques such as supervised fine-tuning (SFT), preference optimization, and reinforcement learning from feedback or verifiable rewards. You will simplify the execution of complex training pipelines, improve experimentation and iteration speed, and enable reusable, production-ready training workflows.

On the inference side, you will drive deep runtime and LLM-serving optimizations to improve throughput, reduce latency, and lower the cost of serving large models. You will optimize execution across heterogeneous hardware and model architectures, leveraging techniques such as continuous batching, KV-cache optimization, quantization, speculative decoding, disaggregated prefill and decode, and efficient distributed inference.

Across both areas, you will improve the performance, scalability, reliability, and developer experience of AI training and inference workloads. You will work closely with AI practitioners to identify bottlenecks across model execution, scheduling, communication, and resource utilization, translating these insights into end-to-end improvements in model development and serving efficiency.

Minimum Qualifications 6+ years of experience building distributed systems, AI platforms, model execution frameworks, or high-performance software systems.

Strong hands-on experience in at least one of the following areas: large-scale AI training, LLM post-training and reinforcement learning, or LLM inference and serving optimization.

Proficiency in Python and experience with a systems-level language such as C++, Rust, Go, or Java.

Experience with PyTorch and modern foundation model architectures, including the execution and performance requirements of large language models.

Understanding of distributed execution and parallelism strategies for large models, including data, tensor, pipeline, or expert parallelism.

Experience profiling and optimizing AI workloads across GPUs or other accelerators, with the ability to identify and resolve…