Machine Learning Engineer - Model Inference

Apple • Cupertino, CA

9h

About The Position

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!

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!

Requirements

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
• At least 2 years of post graduate work experience.
• Strong programming skills in Python.
• Strong programming skills in 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.
• Familiarity with 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.
• Ability to communicate clearly.
• Ability to work effectively with engineers across multiple disciplines.

Nice To Haves

• 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.
• Curiosity about how software, models, and hardware interact to determine real-world performance.

Responsibilities

• Design, build, and operate services used to deploy and serve machine learning models at scale.
• Oversee the infrastructure that powers model inference.
• Develop high-performance serving systems.
• Implement optimization techniques to reduce latency, increase throughput, and improve hardware utilization.
• Collaborate with machine learning researchers, infrastructure engineers, and product teams to transform new models into reliable, production-ready experiences.
• Communicate technical ideas clearly and present design decisions and performance findings to technical and cross-functional audiences.
• Participate in collaborative discussions, design reviews, and project planning meetings.
• Learn quickly, take ownership of meaningful projects, and contribute ideas that improve system performance and user experiences.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level