Applied AI/ML Scientist

Cerebras
United Arab Emirates
Workplace: OnsiteFull timeFunction: Research & Scientific (R&D)Education: mastersSkills: ["Communication","Collaboration","Independent work","Presenting technical results","Stakeholder management"]

Build and customize large language models and large-scale deep learning systems for customer AI use cases within the FieldML team. Own end-to-end model training recipes—from data preprocessing and tokenization to hyperparameter tuning and loss-curve analysis—plus post-training alignment (RLHF/DPO). Translate customer requirements into precise training approaches, scale workloads across Cerebras clusters, and collaborate internally to turn customer wins into reusable playbooks.

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FursaFursa
Cerebras
Cerebras
8 months ago

Applied AI/ML Scientist

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Source: Company careers pageValidated by: Fursa AI
Last checked: 16 hours agoStatus: Live

Job Summary

Build and customize large language models and large-scale deep learning systems for customer AI use cases within the FieldML team. Own end-to-end model training recipes—from data preprocessing and tokenization to hyperparameter tuning and loss-curve analysis—plus post-training alignment (RLHF/DPO). Translate customer requirements into precise training approaches, scale workloads across Cerebras clusters, and collaborate internally to turn customer wins into reusable playbooks.
Location: United Arab Emirates
Workplace: Onsite
Employment Type: Full time
Job Function: Research & Scientific (R&D)

Key Responsibilities

  • •Collaborate with customer stakeholders to identify AI approaches to business problems and scope technical engagements, including feasibility and data readiness.
  • •Architect and execute end-to-end training recipes for custom models, tailoring architectures and recipes to meet customer performance and accuracy requirements.
  • •Design adaptation strategies such as continuous pre-training on private datasets, supervised fine-tuning (SFT), and post-training alignment via RLHF or DPO.
  • •Own the training pipeline, including high-performance data preprocessing, tokenization, hyperparameter tuning, and loss-curve analysis; analyze convergence and gradient stability.
  • •Scale training workloads across Cerebras clusters and build/optimize agentic-system components for tool use, long-context reasoning, and multi-step planning.

Key Requirements

  • •Master’s or PhD in Computer Science, Machine Learning, or a related field.
  • •Expert-level understanding of modern model architectures (e.g., dense transformers, MoEs, multimodal/sequence models) with knowledge of scaling laws and training dynamics.
  • •Proven track record training and/or fine-tuning large models (1B+ parameters), including experience with large-scale model training challenges.
  • •Strong engineering proficiency with Python and PyTorch, plus experience with distributed training frameworks and large-scale distributed data pipelines.
  • •Excellent interpersonal and communication skills to collaborate in fast-paced teams and present complex technical results to diverse audiences.
Experience:AIMachine learningDeep learningLarge language modelsDistributed training
Education:Master's in Computer Science, Machine Learning
Skills:CommunicationCollaborationIndependent workPresenting technical resultsStakeholder management
Tech Stack:PythonPyTorchPyTorch distributedRLHFDPOTokenizationHyperparameter tuningDistributed trainingData preprocessing

Company Brief

Cerebras
Designs and builds wafer-scale AI accelerators and systems for large-scale deep learning workloads, delivering specialized hardware and software to accelerate model training and inference for enterprises and research institutions.
Industry: Hardware Devices
Company Size: Large (251 to 1,000 employees)
Growth: Scaleup
Headquarters: Sunnyvale, United States
Founded: 2016
WebsiteLinkedIn