> Markdown version of [/jobs/ext/1428326-applied-machine-learning-research-scientist](https://www.wearedevelopers.com/jobs/ext/1428326-applied-machine-learning-research-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied Machine Learning Research Scientist - **Company:** Cerebras Systems - **Location:** United States - **Experience:** Experienced - **Contract:** Internship / Graduate position - **Skills:** Big Data, Computer Programming, Software Debugging, Distributed Computing Environment, Python (Programming Language), Machine Learning, Open Source Technology, Tensorflow, Reinforcement Learning, Pytorch, Large Language Models, Deep Learning, Information Technology, Machine Learning Operations, Data Pipelines - **Published:** July 24, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=efeb54f277cc563e ## About the Role * 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 Skills & 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 ## Description 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 * Apply post-training techniques (e.g. RLVR, RLHF, GRPO etc.) techniques to improve model performance. * Build and maintain evaluation pipelines to measure model performance across tasks and domains. * Debug issues across the ML stack, including data pipelines, training jobs, model outputs and mixed or lower precision computation. * Collaborate with researchers to translate ML ideas into efficient, scalable implementation. * Design, implement, and scale ML pipelines across all stages of LLM development (pretraining, fine-tuning, alignment). * Work with large datasets, including dataset generation, filtering, and synthetic data approaches. * Optimize training and inference workflows for performance, efficiency, and reliability. * Contribute high-quality, maintainable code to shared ML infrastructure. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Developing an AI.SDK](https://www.wearedevelopers.com/videos/198-developing-an-ai-sdk) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)