Anthropic Limited
Software Engineer
Anthropic Limited, San Francisco, California, United States, 94199
You may be a good fit if you:
Have significant software engineering experienceAre results-oriented, with a bias towards flexibility and impactPick up slack, even if it goes outside your job descriptionEnjoy pair programming (we love to pair!)Want to learn more about machine learning researchCare about the societal impacts of your workStrong candidates may also have experience with:
High performance, large-scale ML systemsGPUs, Kubernetes, Pytorch, or OS internalsLanguage modeling with transformersReinforcement learningLarge-scale ETLHave security and privacy best practice expertiseExperience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCLLow level systems experience, for example linux kernel tuning and eBPFTechnical expertise: Quickly understanding systems design tradeoffs, keeping track of rapidly evolving software systemsRepresentative projects:
Optimizing the throughput of a new attention mechanismComparing the compute efficiency of two Transformer variantsMaking a Wikipedia dataset in a format models can easily consumeScaling a distributed training job to thousands of GPUsWriting a design doc for fault tolerance strategiesCreating an interactive visualization of attention between tokens in a language modelDeadline to apply:
None. Applications will be reviewed on a rolling basis.
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Have significant software engineering experienceAre results-oriented, with a bias towards flexibility and impactPick up slack, even if it goes outside your job descriptionEnjoy pair programming (we love to pair!)Want to learn more about machine learning researchCare about the societal impacts of your workStrong candidates may also have experience with:
High performance, large-scale ML systemsGPUs, Kubernetes, Pytorch, or OS internalsLanguage modeling with transformersReinforcement learningLarge-scale ETLHave security and privacy best practice expertiseExperience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCLLow level systems experience, for example linux kernel tuning and eBPFTechnical expertise: Quickly understanding systems design tradeoffs, keeping track of rapidly evolving software systemsRepresentative projects:
Optimizing the throughput of a new attention mechanismComparing the compute efficiency of two Transformer variantsMaking a Wikipedia dataset in a format models can easily consumeScaling a distributed training job to thousands of GPUsWriting a design doc for fault tolerance strategiesCreating an interactive visualization of attention between tokens in a language modelDeadline to apply:
None. Applications will be reviewed on a rolling basis.
#J-18808-Ljbffr