Applied Machine Learning Research Scientist

Responsibilities

• Apply post-training techniques (e.g., RLVR, RLHF, GRPO) to improve model performance
• Design, implement, and scale ML pipelines across all stages of LLM development, including pretraining, fine-tuning, and alignment
• Build and maintain evaluation pipelines to measure model performance across various tasks and domains
• Debug issues across the ML stack, including data pipelines, training jobs, and mixed-precision computation
• Collaborate with researchers to translate cutting-edge ML ideas into efficient, scalable implementations
• Optimize training and inference workflows for maximum performance, efficiency, and reliability
• Work with large-scale datasets, including dataset generation, filtering, and synthetic data approaches

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• 0-5 years of experience working with machine learning systems (including internships, research, or industry experience)
• Strong programming skills in Python
• Experience with ML frameworks such as PyTorch
• Solid understanding of machine learning fundamentals
• Familiarity with deep learning architectures, particularly transformers
• Ability to read and understand modern ML papers and implement key ideas

Preferred Qualifications

• Experience working with large language models (training, fine-tuning, and evaluation)
• Familiarity with reinforcement learning concepts
• Experience with distributed training frameworks (e.g., FSDP, Megatron)
• Experience working with large-scale datasets and data pipelines
• Experience debugging or optimizing ML systems for performance
• Contributions to meaningful codebases, projects, or open-source systems

About the Company

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, delivering industry-leading training and inference speeds. Our technology empowers machine learning users to effortlessly run large-scale ML applications, supporting top model labs, global enterprises, and cutting-edge AI-native startups.