Technical leader building Claude Science, an AI workbench for researchers that integrates literature, databases, scientific computing, analysis, and publication tools. Shape product and architecture from 0→1, partner with scientists and research teams to translate domain needs into shipped features and improved model capabilities.
Build and own ML infrastructure, data pipelines, and tooling for Safeguards research at Anthropic. Focus on fast researcher iteration for training/evaluating lightweight detectors on model internals while ensuring correctness at scale. Requires strong Python, distributed systems, and production infrastructure experience.
Build and maintain large-scale distributed inference systems serving Claude to millions of users. Design intelligent routing, autoscaling, and deployment pipelines across diverse AI accelerators while maximizing compute efficiency for production and research workloads. Requires significant distributed systems experience.
Build and own validation pipelines, CI/CD infrastructure, and platform integrations to launch frontier models and inference features reliably across AWS, GCP, and Azure. Requires strong large-scale distributed systems experience and track record improving release velocity.
Research Engineer advancing RL for silicon chip design at Anthropic. Design RL environments for RTL generation, verification, and physical optimization; requires deep ASIC/FPGA expertise from spec to tapeout.
Build and operate production ML infrastructure powering Claude's safety systems, including critical backend services on the token generation path, SLOs, observability, incident response, and platform-agnostic deployment tooling across 1P and 3P clouds. Requires deep distributed systems and on-call experience at scale; ML/transformer familiarity not required.
Build and run evaluations to measure Claude's capabilities, safety, and performance. Design metrics, implement scalable distributed eval infrastructure and dashboards, debug training runs, and partner with researchers to characterize and improve AI systems.
Build and scale research infrastructure and distributed systems at Anthropic to accelerate ML research workflows. Independently lead complex multi-month projects, drive cross-org alignment, and partner with researchers in a fast-evolving, ambiguous environment.
Research Engineer developing novel evaluation frameworks and training strategies for AI systems in life sciences and biology. Requires experience training/evaluating LLMs, Python/ML proficiency, and data pipeline expertise; biology background preferred but not required.
Research Engineer advancing Claude's computer use capabilities through experiments, RL environments, evaluations, and infrastructure for perception and agentic tasks. Requires Python, ML training/evaluation experience, and a focus on safe AI.
Research engineer/scientist building and evaluating vision capabilities for Claude models. Requires 7+ years ML/computer vision experience and work across pretraining, RL, and agentic infrastructure.
Research engineer on the Safeguards Labs team building and evaluating novel safety methods to detect misuse, strengthen model safeguards, and reduce real-world harm from Claude.
Own end-to-end data strategy and RL environment creation for domain-specific knowledge work (finance, healthcare, legal). Combine applied research with hands-on data sourcing, vendor management, and model performance measurement.
Technical lead for the shared, accelerator-agnostic inference runtime serving Claude. Owns architecture, performance, and validation for GPU/TPU/Trainium platforms in a high-scale distributed systems environment.
Research Engineer advancing Claude's code generation capabilities through reinforcement learning. Design RL environments, build verifiers, run training experiments on frontier models, and improve training pipelines for real software engineering tasks.
Build evaluation infrastructure and datasets to measure how well AI agents detect misuse and policy violations. Design experiments, productionize evals into release pipelines, and improve safety investigation capabilities.
Build and maintain distributed inference systems serving Claude to millions of users. Design intelligent routing, autoscaling, and high-performance infrastructure across diverse AI accelerators.
The Software Engineer, RL Data will build systems for high-quality reinforcement learning data for Claude, focusing on data collection, human feedback tooling, and quality assurance. This role involves end-to-end ownership of the stack, pipeline engineering, and supporting research teams.
Designs new information architectures for LLMs to interact with external data sources, implements finetuning/RL training, builds evaluation sets, and develops agentic search capabilities. Requires strong Python/ML skills and LLM experience.
Builds and optimizes RL training infrastructure, removes bottlenecks in the RL stack, and partners with researchers to accelerate model development at scale. Requires strong software engineering, ML infra experience, and comfort across the stack.
Fellows develop and optimize ML systems and performance infrastructure for AI models, focusing on scaling, efficiency, and reliability in a remote-friendly program.
Research Engineer on the Code RL team advancing AI models' ability to write efficient code for accelerators. Requires deep expertise in accelerators like CUDA/ROCm and ML frameworks like JAX/PyTorch, plus experience across kernels, model code, and distributed systems.
Build and optimize large-scale ML systems for safe, steerable AI, handling infrastructure, experiments, and dev tooling. Requires strong software engineering and interest in ML research.
Software Engineer focused on AI reliability engineering, improving robustness of Claude's serving infrastructure across SDK to accelerators. Partners cross-team on SLOs, monitoring, high-availability systems, incident response, and safeguard models.
Software Engineer on Claude Code team builds evaluation systems, tooling, and infrastructure to enhance AI coding capabilities. Collaborates with researchers in fast-paced environment; requires 5+ years experience building complex systems.
Build ML systems to detect and mitigate AI misuse, including classifiers for anomalous behavior, multi-exchange harm monitoring, and agentic safety evaluations. Requires 4+ years ML experience, Python proficiency, and research-to-deployment skills.
Designs and optimizes TPU kernels to address performance issues in ML research, training, and inference systems. Provides feedback on model impacts and solves large-scale systems problems, requiring deep accelerator expertise.
Technical leader building Claude Science, an AI workbench for researchers that integrates literature, databases, scientific computing, analysis, and publication tools. Shape product and architecture from 0→1, partner with scientists and research teams to translate domain needs into shipped features and improved model capabilities.
Build and own ML infrastructure, data pipelines, and tooling for Safeguards research at Anthropic. Focus on fast researcher iteration for training/evaluating lightweight detectors on model internals while ensuring correctness at scale. Requires strong Python, distributed systems, and production infrastructure experience.
Build and maintain large-scale distributed inference systems serving Claude to millions of users. Design intelligent routing, autoscaling, and deployment pipelines across diverse AI accelerators while maximizing compute efficiency for production and research workloads. Requires significant distributed systems experience.
Build and own validation pipelines, CI/CD infrastructure, and platform integrations to launch frontier models and inference features reliably across AWS, GCP, and Azure. Requires strong large-scale distributed systems experience and track record improving release velocity.
Research Engineer advancing RL for silicon chip design at Anthropic. Design RL environments for RTL generation, verification, and physical optimization; requires deep ASIC/FPGA expertise from spec to tapeout.
Build and operate production ML infrastructure powering Claude's safety systems, including critical backend services on the token generation path, SLOs, observability, incident response, and platform-agnostic deployment tooling across 1P and 3P clouds. Requires deep distributed systems and on-call experience at scale; ML/transformer familiarity not required.
Build and run evaluations to measure Claude's capabilities, safety, and performance. Design metrics, implement scalable distributed eval infrastructure and dashboards, debug training runs, and partner with researchers to characterize and improve AI systems.
Build and scale research infrastructure and distributed systems at Anthropic to accelerate ML research workflows. Independently lead complex multi-month projects, drive cross-org alignment, and partner with researchers in a fast-evolving, ambiguous environment.
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Research Engineer developing novel evaluation frameworks and training strategies for AI systems in life sciences and biology. Requires experience training/evaluating LLMs, Python/ML proficiency, and data pipeline expertise; biology background preferred but not required.
Research Engineer advancing Claude's computer use capabilities through experiments, RL environments, evaluations, and infrastructure for perception and agentic tasks. Requires Python, ML training/evaluation experience, and a focus on safe AI.
Research engineer/scientist building and evaluating vision capabilities for Claude models. Requires 7+ years ML/computer vision experience and work across pretraining, RL, and agentic infrastructure.
Research engineer on the Safeguards Labs team building and evaluating novel safety methods to detect misuse, strengthen model safeguards, and reduce real-world harm from Claude.
Own end-to-end data strategy and RL environment creation for domain-specific knowledge work (finance, healthcare, legal). Combine applied research with hands-on data sourcing, vendor management, and model performance measurement.
Technical lead for the shared, accelerator-agnostic inference runtime serving Claude. Owns architecture, performance, and validation for GPU/TPU/Trainium platforms in a high-scale distributed systems environment.
Research Engineer advancing Claude's code generation capabilities through reinforcement learning. Design RL environments, build verifiers, run training experiments on frontier models, and improve training pipelines for real software engineering tasks.
Build evaluation infrastructure and datasets to measure how well AI agents detect misuse and policy violations. Design experiments, productionize evals into release pipelines, and improve safety investigation capabilities.
Build and maintain distributed inference systems serving Claude to millions of users. Design intelligent routing, autoscaling, and high-performance infrastructure across diverse AI accelerators.
The Software Engineer, RL Data will build systems for high-quality reinforcement learning data for Claude, focusing on data collection, human feedback tooling, and quality assurance. This role involves end-to-end ownership of the stack, pipeline engineering, and supporting research teams.
Designs new information architectures for LLMs to interact with external data sources, implements finetuning/RL training, builds evaluation sets, and develops agentic search capabilities. Requires strong Python/ML skills and LLM experience.
Builds and optimizes RL training infrastructure, removes bottlenecks in the RL stack, and partners with researchers to accelerate model development at scale. Requires strong software engineering, ML infra experience, and comfort across the stack.
Fellows develop and optimize ML systems and performance infrastructure for AI models, focusing on scaling, efficiency, and reliability in a remote-friendly program.
Research Engineer on the Code RL team advancing AI models' ability to write efficient code for accelerators. Requires deep expertise in accelerators like CUDA/ROCm and ML frameworks like JAX/PyTorch, plus experience across kernels, model code, and distributed systems.
Build and optimize large-scale ML systems for safe, steerable AI, handling infrastructure, experiments, and dev tooling. Requires strong software engineering and interest in ML research.
Software Engineer focused on AI reliability engineering, improving robustness of Claude's serving infrastructure across SDK to accelerators. Partners cross-team on SLOs, monitoring, high-availability systems, incident response, and safeguard models.
Software Engineer on Claude Code team builds evaluation systems, tooling, and infrastructure to enhance AI coding capabilities. Collaborates with researchers in fast-paced environment; requires 5+ years experience building complex systems.
Build ML systems to detect and mitigate AI misuse, including classifiers for anomalous behavior, multi-exchange harm monitoring, and agentic safety evaluations. Requires 4+ years ML experience, Python proficiency, and research-to-deployment skills.
Designs and optimizes TPU kernels to address performance issues in ML research, training, and inference systems. Provides feedback on model impacts and solves large-scale systems problems, requiring deep accelerator expertise.