Applied Machine Learning Research Scientist

About the role

Cerebras is looking for an Applied Machine Learning Research Scientist to turn modern ML techniques into scalable, high‑performance systems. You will work at the intersection of modeling and systems, focusing on implementing and optimizing large language models (LLMs) for training, fine‑tuning, and reinforcement‑learning‑based post‑training on the world’s largest AI chip.

Key responsibilities

• Apply post‑training techniques such as RLVR, RLHF, and GRPO to improve model performance.
• Build and maintain evaluation pipelines that measure model performance across tasks and domains.
• Debug issues across the ML stack, including data pipelines, training jobs, model outputs, and mixed‑precision computation.
• Collaborate with researchers to translate ML ideas into efficient, scalable implementations.
• Design, implement, and scale ML pipelines for pretraining, fine‑tuning, and alignment of LLMs.
• Work with large datasets, including generation, filtering, and synthetic‑data approaches.
• Optimize training and inference workflows for performance, efficiency, and reliability.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 4+ years of experience working with machine‑learning systems, including internships, research, or industry work.

Required skills

• Strong programming in Python.
• Experience with PyTorch and transformer architectures.
• Familiarity with large language models and reinforcement‑learning concepts.
• Knowledge of distributed training frameworks such as FSDP or Megatron.
• Experience handling large‑scale datasets and data pipelines.
• Ability to debug and optimize ML systems for performance.

What we offer

• Opportunity to work on a breakthrough AI platform beyond GPU constraints.
• Access to one of the fastest AI supercomputers in the world.
• Publication and open‑source of cutting‑edge AI research.
• Startup vitality with job stability.
• Inclusive, non‑corporate work culture that respects individual beliefs.