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Own end-to-end enablement for the Claude Developer Platform: build demos, hands-on labs, and self-service resources; deliver and scale live technical training; coach technical sellers; stress-test features with product; and measure content effectiveness to improve field readiness.

As an Engineering Manager, you'll lead efforts to enhance model performance, manage team projects, and support professional growth in a dynamic environment.

Build and maintain low-level networking software that interfaces accelerators and high-speed networks. Diagnose and optimize kernel- and user-space networking (layers 2-4), implement collectives/RPC, benchmark and improve congestion control, and develop drivers and protocols to reduce latency for distributed ML workloads.

Own technical execution of live and hybrid events, serving as technical director for productions from intake through follow-up. Operate video switchers, audio consoles, cameras, encoders and streaming; build run-of-show and runbooks; perform preventative maintenance and room health checks; scope events, staff productions, coordinate contractors, and communicate technical status to nontechnical stakeholders.

Own end-to-end silicon program activities including specification, technology/IP selection, architecture tradeoffs, hardware-software co-design, and first-silicon bring-up/debug. Partner with external ASIC/IP/foundry partners, build AI-assisted design workflows, and drive directional technical decisions across chip domains from front-end to packaging and test.

Lead operations for high-visibility communications programs, turning ambiguous, fast-moving projects into clear workstreams, owners, timelines, and decisions. Drive alignment across cross-functional partners, design and improve playbooks and crisis protocols, anticipate and remove bottlenecks, and provide strategic program leadership for launches and reactive situations.

Build, maintain, and improve algorithms and infrastructure for finetuning and RL training (RLHF) of large language models. Improve speed, reliability, and usability of training pipelines through profiling, instrumentation, debugging GIL contention, launching test jobs, and implementing research algorithms to support production and internal model training.

Lead system-level architecture for large-scale AI compute: define specs, evaluate vendor designs, drive interconnect, power, thermal, and reliability strategies, develop validation and bring-up plans, and partner with ML, infrastructure, and supply chain teams.

Design and build real-time risk decisioning and tooling to prevent payments fraud. Implement fraud signals at scale, manage dispute and chargeback workflows, monitor metrics (loss, dispute, approval, false positives), lead investigations into emerging fraud patterns, and collaborate cross-functionally with finance, support, legal, data science, and external payment partners.

Build and maintain the shared web platform for Claude.ai: shared UI components, BFF APIs, build/deploy pipelines, and observability. Improve developer velocity, performance, reliability, and documentation to enable other product teams to ship quickly and reliably.