Machine Learning Systems Engineer, RL Engineering

NLP PEOPLE; Los Angeles (CA); On-site; USD 125,000 - 150,000; Full time

Job summary

An innovative firm is seeking a talented ML Systems Engineer to join their Reinforcement Learning Engineering team. In this role, you will develop cutting-edge systems that enhance AI model training, focusing on performance and reliability. You will play a crucial part in empowering researchers to achieve breakthroughs in AI capabilities. If you have a passion for machine learning and enjoy working on impactful systems, this opportunity is perfect for you. Join a team dedicated to creating beneficial AI systems and make a significant contribution to the future of technology.

Qualifications

• 4+ years of software engineering experience required.
• Strong candidates may have experience with large scale systems.

Responsibilities

• Build and improve algorithms and systems for training AI models.
• Focus on performance, robustness, and usability of AI systems.

Skills

Software Engineering

Machine Learning

Reinforcement Learning

Python

Distributed Systems

LLM Training

Education

Bachelor's Degree in Computer Science or related field

Master's Degree in Machine Learning or related field

Job description

About the role:

You want to build the cutting-edge systems that train AI models like Claude. You’re excited to work at the frontier of machine learning, implementing and improving advanced techniques to create ever more capable, reliable and steerable AI. As an ML Systems Engineer on our Reinforcement Learning Engineering team, you’ll be responsible for the critical algorithms and infrastructure that our researchers depend on to train models. Your work will directly enable breakthroughs in AI capabilities and safety. You’ll focus obsessively on improving the performance, robustness, and usability of these systems so our research can progress as quickly as possible. You’re energized by the challenge of supporting and empowering our research team in the mission to build beneficial AI systems.

Our finetuning researchers train our production Claude models, and internal research models, using RLHF and other related methods. Your job will be to build, maintain, and improve the algorithms and systems that these researchers use to train models. You’ll be responsible for improving the speed, reliability, and ease-of-use of these systems.

You may be a good fit if you:

1. Have 4+ years of software engineering experience
2. Like working on systems and tools that make other people more productive
3. Are results-oriented, with a bias towards flexibility and impact
4. Pick up slack, even if it goes outside your job description
5. Enjoy pair programming (we love to pair!)
6. Want to learn more about machine learning research
7. Care about the societal impacts of your work

Strong candidates may also have experience with:

1. High performance, large scale distributed systems
2. Large scale LLM training
3. Python
4. Implementing LLM finetuning algorithms, such as RLHF

Representative projects:

1. Profiling our reinforcement learning pipeline to find opportunities for improvement
2. Building a system that regularly launches training jobs in a test environment so that we can quickly detect problems in the training pipeline
3. Making changes to our finetuning systems so they work on new model architectures
4. Building instrumentation to detect and eliminate Python GIL contention in our training code
5. Diagnosing why training runs have started slowing down after some number of steps, and fixing it
6. Implementing a stable, fast version of a new training algorithm proposed by a researcher

Deadline to apply: None. Applications will be reviewed on a rolling basis.

Company:

Anthropic

Qualifications:

Educational level:

Level of experience (years): Senior (5+ years of experience)