Anthropic Limited
Sr. Software Engineer, Infrastructure
Anthropic Limited, San Francisco, California, United States, 94199
About the role:
Anthropic is seeking talented and experienced Infrastructure Engineers to join our team and support the development, scaling, and maintenance of our cutting-edge AI systems. By joining our Infrastructure team, you will have the opportunity to work on groundbreaking AI technologies and contribute to the development of frontier models, supporting Anthropic's mission to create safe and reliable AI systems that benefit humanity.
We currently have openings on:
Data Infrastructure:
The Data Infrastructure team is responsible for designing, building, and maintaining the data infrastructure that powers our AI research and products. You will collaborate with cross-functional teams to understand data requirements, deliver efficient and reliable data solutions, and continuously improve our data infrastructure. Your role will involve building and optimizing data pipelines, implementing data governance best practices, monitoring and troubleshooting, and setting technical strategies for high-scale, reliable data infrastructure and pipelines. You will work with technologies such as Spark, Airflow, dbt, and cloud services from GCP and AWS, while designing processes to ensure effective team operation and continuous improvement.
Research Infrastructure:
The research infra team addresses the problem of developing and scaling systems that enable researchers to iterate quickly and also scale key systems/ components used by researchers during the development phase to work at production scale as our model footprint grows.
Site Reliability Engineering:
As an SRE at Anthropic, you will design and implement scalable solutions, collaborate with development teams to improve infrastructure reliability, and establish monitoring systems, SLOs, and SLIs. You will implement fault-tolerant design patterns, build automation tools, and participate in an on-call rotation. Utilizing IaC principles, you will collaborate with cross-functional teams to ensure reliability and scalability in new features and services, and accelerate engineering reliability through excellent tooling.
Systems:
The systems team is responsible for supporting some of the largest, most sophisticated clusters in industry used to train, research, and ultimately serve AI models. Your work will be crucial in ensuring Anthropic is able to continue reliably and safely training frontier models. You will be responsible for building systems and running large Kubernetes clusters with GPU/TPU/Tranium workloads.
Observability:
Observability team is responsible for designing, building, and maintaining the observability infrastructure that ensures the reliability, performance, and efficiency of our AI systems and services. You will collaborate with cross-functional teams to understand their observability requirements and deliver solutions using technologies such as Prometheus, Splunk, Cloud Logging, Grafana, and Honeycomb. Your role will involve developing a config-driven approach to manage dashboards and alerts, implementing structured logging and tracing, optimizing the observability stack, and building a reliable system that requires minimal maintenance. You will foster a culture of operational excellence, proactive monitoring, and continuous improvement by providing managed, centralized, and usable observability tools.
Responsibilities:
Lead build out of industry-leading AI clusters (thousands to hundreds of thousands of machines), partnering closely with cloud service providers on cluster build out and required features
Consult with different stakeholders to deeply understand infrastructure, data and compute needs, identifying potential solutions to support frontier research and product development
Set technical strategy and oversee development of high scale, reliable infrastructure systems.
Mentor top technical talent
Design processes (e.g. postmortem review, incident response, on-call rotations) that help the team operate effectively and never fail the same way twice
You may be a good fit if you:
Have 8+ years of relevant industry experience, 3+ years leading large scale, complex projects or teams as an engineer or tech lead
Are obsessed with distributed systems at scale, infrastructure reliability, scalability, security, and continuous improvement
Strong proficiency in at least one programming language (e.g., Python, Rust, Go, Java)
Strong problem-solving skills and ability to work independently
Have a passion for supporting internal partners like research to understand their needs
Have excellent communication skills to build consensus with stakeholders, both internally and externally
Possess deep knowledge of modern cloud infrastructure including Kubernetes, Infrastructure as Code, AWS, and GCP
Strong candidates may also:
Have security and privacy best practice expertise
Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
Low level systems experience, for example linux kernel tuning and eBPF
Technical expertise: Quickly understanding systems design tradeoffs, keeping track of rapidly evolving software systems
Deadline to apply: None. Applications will be reviewed on a rolling basis.
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Anthropic is seeking talented and experienced Infrastructure Engineers to join our team and support the development, scaling, and maintenance of our cutting-edge AI systems. By joining our Infrastructure team, you will have the opportunity to work on groundbreaking AI technologies and contribute to the development of frontier models, supporting Anthropic's mission to create safe and reliable AI systems that benefit humanity.
We currently have openings on:
Data Infrastructure:
The Data Infrastructure team is responsible for designing, building, and maintaining the data infrastructure that powers our AI research and products. You will collaborate with cross-functional teams to understand data requirements, deliver efficient and reliable data solutions, and continuously improve our data infrastructure. Your role will involve building and optimizing data pipelines, implementing data governance best practices, monitoring and troubleshooting, and setting technical strategies for high-scale, reliable data infrastructure and pipelines. You will work with technologies such as Spark, Airflow, dbt, and cloud services from GCP and AWS, while designing processes to ensure effective team operation and continuous improvement.
Research Infrastructure:
The research infra team addresses the problem of developing and scaling systems that enable researchers to iterate quickly and also scale key systems/ components used by researchers during the development phase to work at production scale as our model footprint grows.
Site Reliability Engineering:
As an SRE at Anthropic, you will design and implement scalable solutions, collaborate with development teams to improve infrastructure reliability, and establish monitoring systems, SLOs, and SLIs. You will implement fault-tolerant design patterns, build automation tools, and participate in an on-call rotation. Utilizing IaC principles, you will collaborate with cross-functional teams to ensure reliability and scalability in new features and services, and accelerate engineering reliability through excellent tooling.
Systems:
The systems team is responsible for supporting some of the largest, most sophisticated clusters in industry used to train, research, and ultimately serve AI models. Your work will be crucial in ensuring Anthropic is able to continue reliably and safely training frontier models. You will be responsible for building systems and running large Kubernetes clusters with GPU/TPU/Tranium workloads.
Observability:
Observability team is responsible for designing, building, and maintaining the observability infrastructure that ensures the reliability, performance, and efficiency of our AI systems and services. You will collaborate with cross-functional teams to understand their observability requirements and deliver solutions using technologies such as Prometheus, Splunk, Cloud Logging, Grafana, and Honeycomb. Your role will involve developing a config-driven approach to manage dashboards and alerts, implementing structured logging and tracing, optimizing the observability stack, and building a reliable system that requires minimal maintenance. You will foster a culture of operational excellence, proactive monitoring, and continuous improvement by providing managed, centralized, and usable observability tools.
Responsibilities:
Lead build out of industry-leading AI clusters (thousands to hundreds of thousands of machines), partnering closely with cloud service providers on cluster build out and required features
Consult with different stakeholders to deeply understand infrastructure, data and compute needs, identifying potential solutions to support frontier research and product development
Set technical strategy and oversee development of high scale, reliable infrastructure systems.
Mentor top technical talent
Design processes (e.g. postmortem review, incident response, on-call rotations) that help the team operate effectively and never fail the same way twice
You may be a good fit if you:
Have 8+ years of relevant industry experience, 3+ years leading large scale, complex projects or teams as an engineer or tech lead
Are obsessed with distributed systems at scale, infrastructure reliability, scalability, security, and continuous improvement
Strong proficiency in at least one programming language (e.g., Python, Rust, Go, Java)
Strong problem-solving skills and ability to work independently
Have a passion for supporting internal partners like research to understand their needs
Have excellent communication skills to build consensus with stakeholders, both internally and externally
Possess deep knowledge of modern cloud infrastructure including Kubernetes, Infrastructure as Code, AWS, and GCP
Strong candidates may also:
Have security and privacy best practice expertise
Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
Low level systems experience, for example linux kernel tuning and eBPF
Technical expertise: Quickly understanding systems design tradeoffs, keeping track of rapidly evolving software systems
Deadline to apply: None. Applications will be reviewed on a rolling basis.
#J-18808-Ljbffr