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Applied AI/ML Scientist
Cerebras
, UAE
Full Time
Senior
Machine LearningDeep LearningLarge Language ModelsPyTorchPythonDistributed Training
Free
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Machine LearningDeep LearningLarge Language Models
About the Role
Cerebras Systems seeks an Applied AI Scientist to develop and customize large language models for customer problems. The role involves end-to-end model training, fine-tuning, and building agentic systems.
Key Skills for This Role
Machine LearningDeep LearningLarge Language ModelsPyTorchPythonDistributed Training
Responsibilities
- Collaborate with customer stakeholders to identify AI approaches to business problems
- Contribute to technical scoping of engagements including feasibility analysis and data readiness assessments
- Define project milestones, success metrics, and evaluation benchmarks
- Architect and execute end to end training recipes for custom models
- Design and implement adaptation strategies including continuous pre training, SFT, and RLHF/DPO
- Take ownership of training pipeline from data preprocessing to hyperparameter tuning
- Scale training workloads across Cerebras clusters for multi billion parameter models
- Build and optimize core components of agentic systems (tool use, long context reasoning, multi step planning)
- Serve as AI/ML subject matter expert during technical deep dives with customers
- Act as voice of customer for internal R&D and engineering teams
Requirements
- Master’s or PhD in Computer Science, Machine Learning, or related fields
- Expert level understanding of modern model architectures (dense transformers, MoEs, multimodal, sequence models)
- Proven track record of training and/or fine tuning large models (1B+ parameters)
- Mastery of Python and PyTorch
- Experience with distributed training frameworks and large scale data processing pipelines
- Strong interpersonal and communication skills
Full Job Posting
About Cerebras
- Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs.
- This architecture delivers industry leading training and inference speeds; over 10 times faster than GPU based hyperscale cloud inference services.
- Cerebras works with 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.
About The Role
- As an Applied AI Scientist in the FieldML team, you will develop and customize large language models and large scale deep learning models to solve specific customer problems.
- 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).
- We are looking for experienced AI Scientists passionate about the applied side of machine learning.
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 technical scoping of engagements, including feasibility analysis, data quality/availability/readiness assessments, and 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.
- Design and implement sophisticated adaptation strategies including continuous pre training, SFT, and RLHF/DPO.
- Take full ownership of the training pipeline from data preprocessing to hyperparameter tuning and loss curve analysis.
- Scale training workloads across Cerebras clusters for multi billion parameter models.
- Build and optimize core components of agentic systems focusing on tool use, long context reasoning, and multi step planning.
- Technical Customer Leadership: Serve as AI/ML subject matter expert during technical deep dives.
- Build and maintain strong customer relationships.
- Internal Research and Engineering Collaboration: Act as voice of customer for internal R&D and engineering teams.
- Partner with internal ML teams on prioritization of novel model architectures.
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 Training Experience: Proven track record of training and/or fine tuning large models (1B+ parameters) and direct experience with 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.
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