Software Engineer, ML Networking

Anthropic · 📍 San Francisco, CA | New York City, NY | Seattle, WA · Diposting 2026-10-07

FULL_TIME⭐ TERVERIFIKASI

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Anthropic is seeking a talented Software Engineer to join our team and contribute to our cutting-edge machine learning networking projects. In this role, you will be responsible for designing, developing, and maintaining high-performance networking systems that enable efficient communication between distributed machine learning models. You will work closely with a diverse team of engineers, researchers, and scientists to deliver innovative solutions that push the boundaries of what's possible in the field of artificial intelligence.

📋 Tanggung Jawab Pekerjaan

Design and implement scalable and efficient networking systems for distributed machine learning models,Collaborate with cross-functional teams to integrate machine learning models with networking infrastructure,Optimize network performance and reliability to ensure seamless communication between distributed components,Develop and maintain robust testing frameworks to validate the correctness and performance of networking systems,Stay up-to-date with the latest advancements in machine learning and networking technologies,Mentor and guide junior engineers in the team, fostering a culture of continuous learning and improvement,Contribute to the development of best practices and standards for machine learning networking systems

🎓 Kualifikasi & Syarat

Bachelor's degree in Computer Science, Electrical Engineering, or a related field,3+ years of experience in software engineering, with a focus on networking systems,Strong programming skills in languages such as Python, C++, or Java,Experience with distributed systems and machine learning frameworks,Familiarity with networking protocols and technologies, such as TCP/IP, HTTP, and WebSockets,Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment,Strong communication and interpersonal skills, with the ability to explain technical concepts to non-technical stakeholders