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!

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 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. 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! n

Design implement test and maintain scalable machine learning inference inference latency throughput availability and infrastructure benchmarking and profiling tools to identify performance techniques such as dynamic batching caching quantization pruning model compilation and parallel with machine learning frameworks inference runtimes GPUs and other hardware monitoring logging alerting and load-testing capabilities for production reliability and performance issues across models software runtimes and clear maintainable code and participate in design reviews code reviews and operational support.

Bachelors or Masters degree in Computer Science Computer Engineering Electrical Engineering or a related technical field plus at least 2 years of post graduate work experience. nStrong programming skills in Python and at least one systems-oriented language such as C Rust or understanding of data structures algorithms operating systems and computer with machine learning fundamentals and modern deep learning frameworks such as PyTorch TensorFlow or building debugging or evaluating software systems through coursework internships research open-source contributions or personal to analyze technical problems communicate clearly and work effectively with engineers across multiple disciplines.

Experience with model serving technologies such as Triton TensorRT ONNX Runtime vLLM TensorFlow Serving or with inference optimization techniques including quantization pruning knowledge distillation speculative decoding kernel fusion or continuous of GPUs accelerators distributed systems networking or high-performance with containers Kubernetes cloud infrastructure and production observability benchmarking large language models vision models or other compute-intensive machine learning curiosity about how software models and hardware interact to determine real-world performance.