Machine Learning Engineer - Model Inference

Join Apple Maps to help build the best map in the world! In this role on our ML

Platform Team, you will leverage advanced deep learning and large language

models to improve the search quality and overall customer experiences across our

various Maps platforms. This role offers amazing opportunities to partner

closely with research and product teams while taking ownership of projects and

delivering measurable results at a global scale!

DESCRIPTION

As a member of our team, you will help design, build, and operate the services

used to deploy and serve machine learning models at scale. You will help oversee

the infrastructure that powers model inference, from developing high-performance

serving systems to implementing optimization techniques that reduce latency,

increase throughput, and improve hardware utilization. Get excited about

collaborating closely with machine learning researchers, infrastructure

engineers, and product teams to transform new models into reliable,

production-ready experiences. This role will require you to communicate

technical ideas clearly, and to present design decisions and performance

findings to both technical and cross-functional audiences. You will also

participate in collaborative discussions, design reviews, and project planning

meetings. . We encourage our team-members to learn quickly, take ownership of

meaningful projects, and contribute ideas that improve both the performance of

our systems and the experiences of the people who use them!

MINIMUM QUALIFICATIONS

Bachelor’s or Master’s degree in Computer Science, Computer Engineering,

Electrical Engineering, or a related technical field plus at least 2 years of

post graduate work experience. Strong programming skills in Python and at least

one systems-oriented language such as C++, Rust, or Go. Solid understanding of

data structures, algorithms, operating systems, and computer architecture.

Familiarity with machine learning fundamentals and modern deep learning

frameworks such as PyTorch, TensorFlow, or JAX. Experience building, debugging,

or evaluating software systems through coursework, internships, research,

open-source contributions, or personal projects. Ability to analyze technical

problems, communicate clearly, and work effectively with engineers across

multiple disciplines.

PREFERRED QUALIFICATIONS

Experience with model serving technologies such as Triton, TensorRT, ONNX

Runtime, vLLM, TensorFlow Serving, or TorchServe. Familiarity with inference

optimization techniques, including quantization, pruning, knowledge

distillation, speculative decoding, kernel fusion, or continuous batching.

Understanding of GPUs, accelerators, distributed systems, networking, or

high-performance computing. Familiarity with containers, Kubernetes, cloud

infrastructure, and production observability tools. Experience benchmarking

large language models, vision models, or other compute-intensive machine

learning workloads. Possess curiosity about how software, models, and hardware

interact to determine real-world performance.