Top Tech Jobs & Startup Jobs

Lead the architecture and development of highly available cloud services for AI inference. Build Kubernetes controllers and platform capabilities for workload scheduling, scaling, recovery, deployment, and lifecycle management. Improve reliability, observability, performance, and resource efficiency through production readiness reviews and incident-driven improvements. Guide complex technical initiatives, mentor engineers, resolve infrastructure issues, and align architecture across AI compute, runtime, and product teams.

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.

Design, build, and operate large-scale AI compute infrastructure for training, fine-tuning, evaluation, and inference. Manage Kubernetes clusters, workload scheduling, accelerator enablement, networking, storage, monitoring, and capacity. Troubleshoot complex application, cloud, cluster, and hardware issues while improving reliability, performance, scalability, and developer productivity. Partner with AI teams to automate provisioning, upgrades, monitoring, and maintenance.

Lead design verification for complex IP, subsystems, and SoCs by establishing verification architecture, creating test plans and methodologies, developing testbenches and test suites, executing functional and coverage verification, reviewing microarchitecture, debugging issues, and coordinating verification teams. The role also develops reconfigurable IP features and collaborates across architecture, design, and engineering teams to deliver product quality and release commitments.

Own strategic customer relationships and commercial opportunities for Arm silicon across Physical AI and Edge AI markets. Develop demand, identify lighthouse customers, progress opportunities from evaluation through design wins and deployment, maintain pipeline forecasts, gather market and product insights, and coordinate cross-functional teams. Engage senior business and engineering leaders while communicating Arm’s value in performance, power efficiency, AI capability, security, software ecosystem, and system economics.

Senior SoC Verification Engineer role at Arm. The provided posting contains salary and company policy information but no detailed responsibilities or qualification requirements.

Architects, implements, and validates design-for-test techniques for Arm SoCs and subsystems. Responsibilities include DFT insertion, RTL and gate-level simulation, ATPG pattern generation, coverage debugging, DFT timing analysis, scan insertion, timing closure, ATE test validation, silicon debug, and characterization. The role collaborates with RTL, verification, physical implementation, test, and product engineering teams throughout development and tape-out.

The Senior/Staff Design Verification Engineer will be responsible for design verification in coherent interconnects. Specific responsibilities are not detailed, but expertise in this area is expected.

Architect and design high-volume SoC platforms using Arm IP across mobile, automotive, datacenter, networking and IoT. Lead cross-functional teams to define platform architecture, optimize performance and power, coordinate IP delivery, drive partner engagements, and influence roadmap and feature development. Guide specification maturity and ensure on-time delivery while driving innovation and continuous improvement.