Applied ML Research Scientist: Build Scalable LLM Pipelines

Cerebras; United States; On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Equal opportunity employer

Inclusive work environment

Continuous learning and growth opportunities

Job summary

Cerebras is seeking an Applied Machine Learning Research Scientist to develop scalable, high-performance machine learning systems. The candidate will work on large language models and reinforcement learning techniques, collaborating with researchers.

This role requires extensive experience in machine learning systems and proficiency in Python. The candidate will handle model performance evaluation and system debugging, contributing towards the creation of reliable and effective AI applications.

Qualifications

• 4+ years of experience working with machine learning systems.
• Ability to read and understand modern ML papers and implement key ideas.

Responsibilities

• Apply post-training techniques to improve model performance.
• Build and maintain evaluation pipelines to measure model performance.
• Debug issues across the ML stack, including data pipelines and training jobs.
• Collaborate with researchers to translate ML ideas into efficient implementations.
• Design and implement ML pipelines for all stages of LLM development.
• Optimize training and inference workflows.

Skills

Python programming

Machine learning fundamentals

Experience with ML frameworks such as PyTorch

Understanding of deep learning architectures

Education

Bachelor's or Master’s degree in Computer Science, Engineering, or related field

Job description

Cerebras is seeking an Applied Machine Learning Research Scientist to develop scalable, high-performance machine learning systems. The candidate will work on large language models and reinforcement learning techniques, collaborating with researchers.

This role requires extensive experience in machine learning systems and proficiency in Python. The candidate will handle model performance evaluation and system debugging, contributing towards the creation of reliable and effective AI applications.