Applied AI/ML Scientist

Cerebras

. Develop and customize large language models and large-scale deep learning models for customer problems .

Posted 11/6/2026full-timeAbu Dhabi • United Arab EmiratesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and customizing large language models and deep learning architectures, with a strong focus on training and fine-tuning large-scale models. Proven ability to collaborate with stakeholders, manage projects, and communicate complex technical concepts effectively.

Highest-signal resume keywords

Large Language Model DevelopmentDeep Learning Model CustomizationPython MasteryPyTorch ExpertiseDistributed Training Frameworks Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Dense TransformersMixture of Experts (MoEs)Multimodal ModelsSequence ModelsHyperparameter TuningData PreprocessingTokenizationModel Architecture SelectionLoss-Curve AnalysisGradient Stability Analysis

Soft Skills

Strong Interpersonal SkillsEffective CommunicationAutonomous WorkCollaborative TeamworkProject Management

Tools & Technologies

Cerebras ClustersDistributed Data Processing PipelinesContinuous Pre-TrainingSupervised Fine-TuningReinforcement Learning from Human Feedback (RLHF)Dynamic Programming Optimization (DPO)

Certifications & Qualifications

Master’s or PhD in Computer ScienceMaster’s or PhD in Machine Learning

Industry Keywords

AI ApproachesModel Training ChallengesCustomer Relationship ManagementTechnical ScopingFeasibility Analysis

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Develop and customize large language models and large-scale deep learning models for customer problems
Collaborate with customer stakeholders to identify AI approaches for business problems
Contribute to technical scoping, feasibility analysis, data quality/readiness assessments, and model architecture selection
Define project milestones, success metrics, and evaluation benchmarks
Architect and execute end-to-end training recipes for custom models
Design and implement continuous pre-training, supervised fine-tuning, RLHF, and DPO strategies
Own data preprocessing, tokenization, hyperparameter tuning, and loss-curve analysis
Analyze model convergence, loss dynamics, and gradient stability on specialized hardware
Scale multi-billion-parameter training workloads across Cerebras clusters
Build and optimize agentic-system components for tool use, long-context reasoning, and multi-step planning
Serve as an AI/ML subject matter expert during technical deep dives
Build and maintain customer relationships
Represent customer needs to internal R&D and engineering teams
Partner with ML and product teams on model architectures, training recipes, and case studies
Distill successful customer projects into internal playbooks

Requirements

What you’ll need

Master’s or PhD in Computer Science, Machine Learning, or related fields
Expert-level understanding of dense transformers, MoEs, multimodal and sequence models, scaling laws and training dynamics
Proven track record of training and/or fine-tuning large models with 1B+ parameters
Direct experience with large-scale model training challenges
Mastery of Python and PyTorch
Experience with distributed training frameworks
Experience with large-scale distributed data processing pipelines and tools
Strong interpersonal and communication skills
Ability to work autonomously and collaboratively in dynamic, fast-paced settings
Ability to manage multiple projects and pivot as customer needs evolve
Ability to present complex technical results to C-level executives and research scientists

Benefits

Comp & perks

Publish and open source cutting-edge AI research
Work on one of the fastest AI supercomputers in the world
Job stability with startup vitality
Simple, non-corporate work culture that respects individual beliefs
Continuous learning, growth and support
Equal opportunity and diverse, inclusive work environment