Machine Learning Systems Engineer, RL Engineering

Position Overview

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for users and society. As an ML Systems Engineer on our Reinforcement Learning Engineering team, you will build, maintain, and improve the critical algorithms and infrastructure used in training models like Claude. Your work will boost system performance, robustness, and usability while enabling breakthroughs in AI capabilities and safety. This role offers the chance to work at the cutting edge of machine learning research in a collaborative, impact-focused environment.

Key Responsibilities

• Develop and optimize the algorithms and systems that support RLHF and model training pipelines.
• Enhance the speed, reliability, and ease-of-use of the training infrastructure.
• Profile reinforcement learning pipelines to identify and implement performance improvements.
• Build systems to automate training job launches and detect issues such as Python GIL contention.
• Implement and refine new training algorithms proposed by researchers.

Required Qualifications

• 4+ years of software engineering experience.
• Bachelor’s degree in a related field or equivalent experience.
• Experience working on systems and tools that enhance productivity.
• Strong results orientation with flexibility and impact in mind.
• Willingness to go beyond the traditional job description and engage in pair programming.
• A keen interest in machine learning research and its societal impacts.

Preferred Qualifications

• Experience with high-performance, large scale distributed systems.
• Hands-on experience in large-scale LLM training.
• Proficiency in Python.
• Experience implementing LLM finetuning algorithms, such as RLHF.

Benefits & Perks

• Compensation: $300,000-$405,000 USD Annual Salary
• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours with a hybrid office policy (minimum 25% in-office)
• Visa sponsorship available