Senior Machine Learning Engineer, Core Systems

AI Breaking Wire; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Benefits offered by this job

Equity

Health benefits

Remote-friendly US culture

Learning stipend

Job summary

Anthropic is seeking a Senior ML Engineer to join the Core Systems team to optimize training and inference infrastructure for the Claude family of foundation models. You will design high-performance distributed pipelines and work with researchers to remove scaling bottlenecks.

Ideal candidates have 5+ years of ML infrastructure experience, expert C++/Python skills, and hands-on work with AWS/GCP and Kubernetes.

Qualifications

• 5+ years of software engineering experience with a focus on large-scale ML infrastructure.
• Expert-level C++ and Python programming.
• Deep familiarity with distributed training paradigms (FSDP, Tensor Parallelism, Pipeline Parallelism).

Responsibilities

• Architect and implement high-performance distributed training and inference pipelines for massive transformer models.
• Collaborate with researchers to unblock scaling bottlenecks and improve hardware utilization.
• Develop low-latency inference runtimes and model optimization techniques (quantization, pruning, kernel fusion).
• Monitor and triage large-scale cluster performance issues across heterogeneous GPU clusters.

Skills

C++

Python

Distributed ML

Kubernetes

AWS / GCP

Tools

PyTorch

TensorFlow

Job description

About the Role

Anthropic is dedicated to building reliable, interpretable, and steerable AI systems. We are seeking a Senior ML Engineer to join our Core Systems team to optimize the training and inference infrastructure for our Claude family of foundation models.

Responsibilities

• Architect and implement high-performance distributed training and inference pipelines for massive transformer models.
• Work hand-in-hand with research scientists to unblock scaling bottlenecks and improve hardware utilization.
• Develop low-latency inference runtimes and model optimization techniques (quantization, pruning, kernel fusion).
• Monitor and triage large-scale cluster performance issues across heterogeneous GPU clusters.

Requirements

• 5+ years of software engineering experience with a strong focus on large-scale machine learning infrastructure.
• Expert-level C++ and Python programming skills.
• Deep familiarity with distributed training paradigms (FSDP, Tensor Parallelism, Pipeline Parallelism).
• Experience working with cloud providers (AWS, GCP) and orchestration tools like Kubernetes.

Benefits

• Competitive salary and generous equity package.
• Premium medical, dental, and vision benefits.
• Flexible working hours and remote-friendly culture within the US.
• Annual learning and development stipend.