Research Scientist driving fundamental biological discoveries by building large-scale computational analysis pipelines, partnering with experimental biologists on hypothesis-driven research, and leveraging frontier AI models like Claude on petabyte-scale biological data. Requires PhD and end-to-end computational biology research track record with demonstrated breadth.
As the first dedicated education researcher at Anthropic's Education Labs, design and run mixed-methods studies on AI skill development, build/validate learning measures and instruments, and translate insights into product, curriculum, and model improvements. Requires research background in learning sciences or related fields, strong mixed-methods expertise, Python/LLM technical skills, and comfort with ambiguity.
Anthropic is seeking a Research Scientist to join their Life Sciences team. This role involves building and shipping agentic tools, designing evaluation benchmarks, and partnering with external users to improve model capabilities on scientific tasks.
Conduct empirical research on AI's economic impacts, labor markets, and societal effects using external data and Python. Produce public outputs under mentorship, focusing on AI safety and policy recommendations; 4-month full-time program open to varying experience levels.
Fellows conduct research and development in reinforcement learning to advance AI systems. Requires strong RL background and Python proficiency for collaborative program projects.
Fellows conduct research on AI safety, collaborating with Anthropic researchers to develop evaluation methods and alignment techniques. Requires strong interest in AI safety, CS/math background, and ML experience.
Builds macroeconomic models and scenario-based forecasting tools for transformative AI impacts on growth, labor markets, and income distribution. Requires PhD in Economics, expertise in macro modeling, computational methods, and grounding in real-world AI usage data.
Leads strategy and execution for measuring AI model capabilities across training and deployment, developing novel long-horizon evaluations and leading a team of researchers. Requires experience in LLM evaluations, technical leadership, and cross-team collaboration in fast-paced AI research environments.
Builds large-scale infrastructure for AI scientist training, evaluation, and deployment, resolving bottlenecks in distributed systems for scientific AGI. Requires 6+ years in infrastructure engineering with expertise in ML stacks, containers, and data pipelines.
Develops next-generation large language models through research, experimentation, and engineering on pre-training team. Requires strong Python/PyTorch skills, ML expertise, and MS/PhD in related field.
Builds next-generation training environments and evaluations for agentic AI models, blending research in reinforcement learning with robust engineering implementation. Requires strong technical judgment, agency, and experience in ML systems.
Research Engineer works end-to-end to remove bottlenecks toward scientific AGI, focusing on long-horizon reasoning, computer use, and model capabilities. Requires 8+ years ML experience, expertise in language model pipelines, distributed systems, and collaborative problem-solving.
Conducts mechanistic interpretability research to reverse-engineer language models, developing methods to understand neural network algorithms for AI safety. Requires scientific research background, Python proficiency, and collaborative engineering mindset.
Conducts experimental ML research on AI alignment and safety for powerful systems, focusing on scalable oversight, control, and stress-testing. Requires strong software/ML engineering, empirical research experience, and Python proficiency.
Research Engineer focused on mechanistic interpretability, building tools and infrastructure to reverse-engineer neural networks for safer AI. Requires 5+ years software experience, Python proficiency, and AI research contributions.
Research Engineer optimizes and scales production pretraining of frontier AI models, handling performance, debugging, experiments, and on-call incidents. Requires expertise in JAX, TPU, PyTorch, or large-scale ML systems with a 50/50 research-engineering balance.
Research Engineer implements and scales post-training techniques like Constitutional AI and RLHF for production AI models, optimizing capabilities, alignment, and safety. Requires strong Python skills, ML systems experience, and ability to handle complex distributed training pipelines.
Research Engineer builds and optimizes reinforcement learning infrastructure for advancing AI capabilities like agentic models, tool use, and reasoning. Requires Python proficiency, ML frameworks experience, and ability to blend research with scalable engineering.
Research Scientist driving fundamental biological discoveries by building large-scale computational analysis pipelines, partnering with experimental biologists on hypothesis-driven research, and leveraging frontier AI models like Claude on petabyte-scale biological data. Requires PhD and end-to-end computational biology research track record with demonstrated breadth.
As the first dedicated education researcher at Anthropic's Education Labs, design and run mixed-methods studies on AI skill development, build/validate learning measures and instruments, and translate insights into product, curriculum, and model improvements. Requires research background in learning sciences or related fields, strong mixed-methods expertise, Python/LLM technical skills, and comfort with ambiguity.
Anthropic is seeking a Research Scientist to join their Life Sciences team. This role involves building and shipping agentic tools, designing evaluation benchmarks, and partnering with external users to improve model capabilities on scientific tasks.
Conduct empirical research on AI's economic impacts, labor markets, and societal effects using external data and Python. Produce public outputs under mentorship, focusing on AI safety and policy recommendations; 4-month full-time program open to varying experience levels.
Fellows conduct research and development in reinforcement learning to advance AI systems. Requires strong RL background and Python proficiency for collaborative program projects.
Fellows conduct research on AI safety, collaborating with Anthropic researchers to develop evaluation methods and alignment techniques. Requires strong interest in AI safety, CS/math background, and ML experience.
Builds macroeconomic models and scenario-based forecasting tools for transformative AI impacts on growth, labor markets, and income distribution. Requires PhD in Economics, expertise in macro modeling, computational methods, and grounding in real-world AI usage data.
Leads strategy and execution for measuring AI model capabilities across training and deployment, developing novel long-horizon evaluations and leading a team of researchers. Requires experience in LLM evaluations, technical leadership, and cross-team collaboration in fast-paced AI research environments.
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Builds large-scale infrastructure for AI scientist training, evaluation, and deployment, resolving bottlenecks in distributed systems for scientific AGI. Requires 6+ years in infrastructure engineering with expertise in ML stacks, containers, and data pipelines.
Develops next-generation large language models through research, experimentation, and engineering on pre-training team. Requires strong Python/PyTorch skills, ML expertise, and MS/PhD in related field.
Builds next-generation training environments and evaluations for agentic AI models, blending research in reinforcement learning with robust engineering implementation. Requires strong technical judgment, agency, and experience in ML systems.
Research Engineer works end-to-end to remove bottlenecks toward scientific AGI, focusing on long-horizon reasoning, computer use, and model capabilities. Requires 8+ years ML experience, expertise in language model pipelines, distributed systems, and collaborative problem-solving.
Conducts mechanistic interpretability research to reverse-engineer language models, developing methods to understand neural network algorithms for AI safety. Requires scientific research background, Python proficiency, and collaborative engineering mindset.
Conducts experimental ML research on AI alignment and safety for powerful systems, focusing on scalable oversight, control, and stress-testing. Requires strong software/ML engineering, empirical research experience, and Python proficiency.
Research Engineer focused on mechanistic interpretability, building tools and infrastructure to reverse-engineer neural networks for safer AI. Requires 5+ years software experience, Python proficiency, and AI research contributions.
Research Engineer optimizes and scales production pretraining of frontier AI models, handling performance, debugging, experiments, and on-call incidents. Requires expertise in JAX, TPU, PyTorch, or large-scale ML systems with a 50/50 research-engineering balance.
Research Engineer implements and scales post-training techniques like Constitutional AI and RLHF for production AI models, optimizing capabilities, alignment, and safety. Requires strong Python skills, ML systems experience, and ability to handle complex distributed training pipelines.
Research Engineer builds and optimizes reinforcement learning infrastructure for advancing AI capabilities like agentic models, tool use, and reasoning. Requires Python proficiency, ML frameworks experience, and ability to blend research with scalable engineering.