Top AI & Machine Learning Jobs

Optimize production AI workloads on Arm edge devices for performance and power efficiency. Develop kernel- and system-level implementations for DNN models using AI optimization tools and profiling techniques. Create production reference implementations and technical documentation, diagnose complex customer performance issues, and collaborate across engineering teams. Translate technical findings into recommendations for engineers and senior leadership while influencing Arm’s IP and software roadmaps.

Lead research and development of perception algorithms for Physical AI, including detection, segmentation, tracking, localization, sensor fusion, and scene understanding. Define rigorous datasets, experiments, metrics, and evaluation pipelines, then optimize models for real-time deployment across Arm CPUs and AI accelerators. Collaborate with robotics, controls, hardware, product, and customer teams; mentor technical staff; guide on-device perception roadmaps; and contribute through publications, patents, and open-source work.

Lead the architecture and hands-on development of distributed AI inference runtime systems. Responsibilities include scheduling, batching, KV-cache and memory management, distributed execution, kernel optimization, profiling, benchmarking, and performance analysis. Enable new model architectures, improve latency and throughput, develop validation and safe-rollout systems, and collaborate with infrastructure, compiler, hardware, cloud, and research teams. Mentor engineers and establish performance-engineering practices for Arm’s production AI platform.

Leads architecture and hands-on development of distributed AI inference runtime capabilities, including scheduling, batching, KV-cache management, memory allocation, kernel optimization, model execution, and performance benchmarking. Partners with infrastructure, compiler, hardware, cloud, and research teams to improve latency, throughput, reliability, and resource efficiency. Provides technical leadership, conducts reviews, mentors engineers, and establishes performance-engineering practices for production AI workloads.

Build foundational platform software, runtime infrastructure, APIs, SDKs, and developer tooling for on-device physical AI workloads. Develop reliable deployment, lifecycle management, observability, and diagnostics systems under edge constraints. Optimize performance across CPUs and AI accelerators while creating stable abstractions over AI frameworks, models, and hardware. Collaborate with architects, product managers, and AI engineers on production-ready platform capabilities, reference implementations, documentation, and workflows.

Drive hardware-aware ML algorithm development and optimization for ARM compute platforms. Analyze ML workloads (PPA focus), prototype algorithms, and optimize state-of-the-art networks for CPU/GPU/NPU targets.

Design and implement scalable platform services, runtimes, and developer tooling for deploying and managing AI workloads on Arm-based edge devices. Build abstractions, observability, and performance optimizations across heterogeneous hardware while collaborating with architects and AI engineers to produce robust, maintainable systems and SDKs for on-device AI.