Anthropic

About Anthropic

Building safe and reliable AI systems for everyone

🏢 Tech👥 1001+ employees📅 Founded 2021📍 SoMa, San Francisco, CA💰 $29.3b4.5
B2BArtificial IntelligenceDeep TechMachine LearningSaaS

Key Highlights

  • Headquartered in SoMa, San Francisco, CA
  • Raised $29.3 billion in funding, including $13 billion Series F
  • Over 1,000 employees focused on AI safety and research
  • Launched Claude, an AI chat assistant rivaling ChatGPT

Anthropic, headquartered in SoMa, San Francisco, is an AI safety and research company focused on developing reliable, interpretable, and steerable AI systems. With over 1,000 employees and backed by Google, Anthropic has raised $29.3 billion in funding, including a monumental Series F round of $13 b...

🎁 Benefits

Anthropic offers comprehensive health, dental, and vision insurance for employees and their dependents, along with inclusive fertility benefits via Ca...

🌟 Culture

Anthropic's culture is rooted in AI safety and reliability, with a focus on producing less harmful outputs compared to existing AI systems. The compan...

Anthropic

Machine Learning Engineer Mid-Level

AnthropicSan Francisco

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Overview

Anthropic is seeking a Machine Learning Systems Engineer to enhance AI model training systems. You'll work with Python and machine learning frameworks to improve algorithms and infrastructure. This role requires 4+ years of software engineering experience.

Job Description

Who you are

You have 4+ years of software engineering experience, with a strong focus on building systems and tools that support machine learning initiatives. Your background includes working with algorithms and infrastructure that enhance the performance and usability of AI systems. You are excited about the challenge of improving the reliability and efficiency of machine learning processes, and you thrive in collaborative environments where you can support research teams. Your experience with Python and machine learning frameworks positions you well to contribute to cutting-edge AI projects. You are passionate about creating systems that are not only effective but also interpretable and steerable, aligning with the mission of building beneficial AI.

Desirable

Experience with reinforcement learning techniques and familiarity with advanced machine learning methodologies will set you apart. You are comfortable working in a fast-paced environment and are eager to tackle complex challenges that arise in the development of AI systems. A proactive approach to problem-solving and a commitment to continuous improvement are essential traits that you bring to the team.

What you'll do

As a Machine Learning Systems Engineer on the Reinforcement Learning Engineering team, you will be responsible for developing and maintaining the critical algorithms and infrastructure that our researchers rely on to train AI models like Claude. Your work will directly contribute to breakthroughs in AI capabilities and safety, focusing on enhancing the performance, robustness, and usability of these systems. You will collaborate closely with finetuning researchers to implement and improve advanced techniques, ensuring that the systems are efficient and user-friendly. Your role will involve building, maintaining, and optimizing the algorithms that facilitate the training of production models and internal research projects. You will be tasked with improving the speed and reliability of these systems, enabling our research team to progress rapidly in their mission to create beneficial AI.

What we offer

At Anthropic, we provide a supportive and collaborative work environment where you can thrive. We offer competitive compensation and benefits, including optional equity donation matching, generous vacation and parental leave, and flexible working hours. Our office in San Francisco is designed for collaboration and creativity, providing a lovely space for you to work alongside committed colleagues. We encourage you to apply even if your experience doesn't match every requirement, as we value diverse perspectives and backgrounds in our team.

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