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As a Performance Engineer, you'll optimize distributed systems for machine learning, solve systems problems, and contribute to high-performance ML projects.

The role involves developing and optimizing tokenization systems for machine learning, collaborating with research teams, and improving model training efficiency.

As a Staff Infrastructure Engineer, you'll develop, optimize, and manage large-scale infrastructure systems and data pipelines to support AI training and scientific research, addressing key infrastructure challenges.

Software engineers at Anthropic will develop safety mechanisms, monitor AI systems, and build abuse detection infrastructure to enhance user well-being and prevent misuse.

Build and maintain cloud cost attribution and cost-of-revenue models, forecast non-accelerator infrastructure demand, define unit cost metrics, surface efficiency opportunities, and create self-serve dashboards and reports to drive capacity planning and cost optimization.

Build and scale AI-based monitoring systems that analyze very large unstructured datasets, produce trustworthy structured insights, and enable human oversight. Work across the stack from core analysis frameworks to user-facing tools, partner with researchers and safety teams, develop agentic integrations for autonomous investigation, and shape team strategy and tooling investments.

Build and operate production data pipelines and observability systems for Anthropic's accelerator fleet. Instrument and analyze compute efficiency, normalize multi-cloud billing/telemetry, and develop internal tooling for capacity planning and workload attribution. Operate Kubernetes-native systems at scale and collaborate across research, infrastructure, inference, and finance teams.

The Technical Solutions Specialist in Data Operations will enhance product strategies through data compliance, engineering, and systems management, collaborating across teams.

Own developer education strategy and roadmap for Anthropic's Claude, creating and launching developer-facing videos, tutorials, courses, and API cookbooks. Partner with product, engineering, DevRel, and marketing; manage producers and writers; run content audits; define metrics tied to developer adoption, activation, and fluency.

The role involves building and improving systems for training AI models, focusing on algorithms and infrastructure for reinforcement learning. Responsibilities include enhancing performance, reliability, and usability of systems to support research.