Applied Machine Learning Research Scientist

<div class="content-intro"><p><span data-contrast="none">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, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. </span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335559685":0,"335559737":240,"335559738":240,"335559739":240,"335559740":279}"> </span></p> <p>Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. <a href="https://openai.com/index/cerebras-partnership/">OpenAI recently announced a multi-year partnership with Cerebras</a>, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. </p> <p>Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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.</p></div><h4>About The Role</h4> <p><span data-contrast="none">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.</span><span data-ccp-props="{"335559738":240,"335559739":240}"> </span></p> <p><span data-contrast="none">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.</span><span data-ccp-props="{"335559738":240,"335559739":240}"> </span></p> <p><span data-contrast="none">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.</span><span data-ccp-props="{"335559738":240,"335559739":240}"> </span></p> <p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 3">Responsibilities</span></span></strong><span data-ccp-props="{"134245418":true,"134245529":true,"335559738":281,"335559739":281}"> </span></p> <ul> <li><span data-contrast="auto">Apply post-training techniques (e.g. RLVR, RLHF, GRPO etc.) techniques to improve model performance.</span></li> <li><span data-contrast="auto">Build and maintain evaluation pipelines to measure model performance across tasks and domains.</span></li> <li><span data-contrast="auto">Debug issues across the ML stack, including data pipelines, training jobs, model outputs and mixed or lower precision computation.</span></li> <li><span data-contrast="auto">Collaborate with researchers to translate ML ideas into efficient, scalable implementation.</span></li> <li><span data-contrast="auto">Design, implement, and scale ML pipelines across all stages of LLM development (pretraining, fine-tuning, alignment).</span></li> <li><span data-contrast="auto">Work with large dataset ... (truncated, view full listing at source)