Applied Machine Learning Research Scientist
Role overview
As an Applied Machine Learning Research Scientist at Cerebras, you will play a key role in turning modern machine learning techniques into scalable, high-performance systems. This role sits at the intersection of modeling and systems focused not on publishing new algorithms, but on understanding how they work and making them run effectively at scale. Your work will directly impact how large language models (LLMs) are trained, optimized, and deployed on one of the most advanced AI platforms in the world.
You will work closely with researchers and senior engineers to implement and improve workflows for LLM pretraining, fine-tuning, and reinforcement learning-based post-training. This includes building training pipelines, debugging complex system behaviors, improving model quality, and iterating on data and evaluation strategies. Your contributions will help translate cutting-edge ML ideas into reliable, production-ready systems that solve real-world problems.
This role is ideal for candidates who enjoy hands-on engineering, want to build deep intuition for ML systems, and are excited about working on LLMs and reinforcement learning in practice, not just in theory.
Responsibilities
- check_circle Apply post-training techniques (e.g. RLVR, RLHF, GRPO etc.) techniques to improve model performance.
- check_circle Build and maintain evaluation pipelines to measure model performance across tasks and domains.
- check_circle Debug issues across the ML stack, including data pipelines, training jobs, model outputs and mixed or lower precision computation.
- check_circle Collaborate with researchers to translate ML ideas into efficient, scalable implementation.
- check_circle Design, implement, and scale ML pipelines across all stages of LLM development (pretraining, fine-tuning, alignment).
- check_circle Work with large datasets, including dataset generation, filtering, and synthetic data approaches.
- check_circle Optimize training and inference workflows for performance, efficiency, and reliability.
- check_circle Contribute high-quality, maintainable code to shared ML infrastructure.
Basic qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 4+ years of experience (including internships, research, or industry experience) working with machine learning systems; we are hiring multiple positions for various levels.
- 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
Benefits
- check_circle Build a breakthrough AI platform beyond the constraints of the GPU.
- check_circle Publish and open source their cutting-edge AI research.
- check_circle Work on one of the fastest AI supercomputers in the world.
- check_circle Enjoy job stability with startup vitality.
- check_circle Our simple, non-corporate work culture that respects individual beliefs.
About the company
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.
This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.
Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.