About The Role

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. 

Skills & Qualifications    

•  Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 0 - 5 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 

About The Role

As an Applied AI Scientist in the FieldML team, you will be responsible for developing and customizing large language models and more broadly large-scale deep learning models to solve specific customer problems. You won't just advise; you will build. You will bridge the gap between state-of-the-art research and real-world applications by helping customers harness the power of the Cerebras Wafer-Scale Engine (WSE) for their AI initiatives.  

We are looking for experienced AI Scientists who are passionate about the "applied" side of machine learning - those who enjoy not just reading papers, but implementing, training, and scaling models to solve complex business and scientific problems. You will work on a diverse range of projects, from training bespoke models from scratch to fine-tuning and optimizing the latest Large Language Models (LLMs) for specific industry verticals, to designing and building components for custom agentic systems. 

The ideal candidate has experience in large model training and/or post-training, a deep understanding of training dynamics and model convergence, and expertise in data curation, combined with strong communication skills.   

Key Responsibilities 

• Customer Use Case Discovery & Project Scoping 
  • Collaborate with customer stakeholders to identify the best approaches to their business problem with AI. 
  • Contribute to the technical scoping of engagements, including feasibility analysis, data quality/availability/readiness assessments, and the selection of optimal model architectures. 
  • Define project milestones, success metrics, and rigorous evaluation benchmarks to ensure the solution delivers measurable value to the customer’s business. 
• Custom SOTA Models and AI Systems Development 
  • Architect and execute end-to-end training recipes for custom models, tailoring model architecture and training recipes to meet customer-specific performance and accuracy requirements. 
  • Design and implement sophisticated adaptation strategies, including continuous pre-training on private datasets, supervised fine-tuning (SFT), and post-training alignment via RLHF or DPO. 
  • Take full ownership of the training pipeline, from high-performance data preprocessing and tokenization to hyperparameter tuning and loss-curve analysis. 
  • Navigate the nuances of model convergence on specialized hardware, performing deep-dive analysis into loss dynamics and gradient stability. 
  • Scale training workloads across Cerebras clusters, ensuring efficient utilization of the hardware for multi-billion parameter models. 
  • Build and optimize the core components of agentic systems, focusing on tool-use capabilities, long-context reasoning, and multi-step planning. 
• Technical Customer Leadership 
  • Serve as an AI/ML subject matter expert during technical deep-dives, translating customer requirements into precise training recipes. 
  • Build and maintain strong customer relationships to become their go-to AI/ML expert. 
• Internal Research and Engineering Collaboration 
  • Act as the "voice of the customer" for internal R&D and engineering teams to drive improvements in our software stack and hardware utilization. 
  • Partner with internal ML teams and product teams on prioritization of novel model architectures with Cerebras software stack, development of training recipes and internal case studies. 
  • Distill customer-facing successful projects into internal playbooks, helping scale the FieldML team’s ability to deliver specialized models.

Skills And Qualifications   

• Education: Master’s or PhD in Computer Science, Machine Learning, or related fields. 
• Broad Deep Learning Expertise: Expert-level understanding of modern model architectures, including dense transformers, MoEs, multimodal and sequence models, scaling laws and training dynamics.  
• Hands-on Trainig Experience: Proven track record of training and/or fine-tuning large models (1B+ parameters) and direct experience with the challenges of large-scale model training.  
• Engineering Proficiency: Mastery of Python and PyTorch, experience with distributed training frameworks and large-scale distributed data processing pipelines and tools. 
• Strong Interpersonal and Communication Skills: Effective in collaborative and fast-paced team settings, able to work autonomously and within a team in a dynamic environment, managing multiple projects and pivoting as customer needs evolve. Able to present complex technical results to diverse audience - from C-level executives to research scientists, and to work collaboratively to solve customers’ unique challenges.