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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.
Job Responsibility:
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
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
Scale training workloads across Cerebras clusters
Build and optimize the core components of agentic systems
Technical Customer Leadership
Serve as an AI/ML subject matter expert during technical deep-dives
Build and maintain strong customer relationships
Internal Research and Engineering Collaboration
Act as the voice of the customer for internal R&D and engineering teams
Partner with internal ML teams and product teams on prioritization of novel model architectures
Distill customer-facing successful projects into internal playbooks
Requirements:
Master’s or PhD in Computer Science, Machine Learning, or related fields
Expert-level understanding of modern model architectures, including dense transformers, MoEs, multimodal and sequence models, scaling laws and training dynamics
Proven track record of training and/or fine-tuning large models (1B+ parameters) and direct experience with the challenges of large-scale model training
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
What we offer:
Build a breakthrough AI platform beyond the constraints of the GPU
Publish and open source their cutting-edge AI research
Work on one of the fastest AI supercomputers in the world
Enjoy job stability with startup vitality
Our simple, non-corporate work culture that respects individual beliefs