Applied AI ML Scientist
Posted Updated 2
views
Summary
Applied AI ML Scientist at Cerebras (UAE): scopes customer AI engagements and builds custom large-scale models and agentic systems — designing training recipes, fine-tuning and aligning models (SFT, RLHF, DPO), analyzing training behavior, and scaling multi-billion-parameter workloads across clusters with Python and PyTorch.
You will scope customer AI engagements and develop custom large-scale models and agentic systems. You will design training recipes, preprocess data, fine-tune and align models, scale workloads across clusters, analyze training behavior, and translate customer needs into technical solutions.
Responsibilities
- Identify AI approaches for customer business problems
- Scope engagements through feasibility and data-readiness assessments
- Define project milestones, success metrics, and evaluation benchmarks
- Architect and execute training recipes for custom models
- Implement continuous pre-training, supervised fine-tuning, RLHF, and DPO strategies
- Own training pipelines from data preprocessing and tokenization through hyperparameter tuning and loss analysis
- Analyze model convergence, loss dynamics, and gradient stability
- Scale multi-billion-parameter training workloads across clusters
- Build components for agentic systems including tool use, long-context reasoning, and multi-step planning
- Translate customer requirements into training recipes
- Share customer feedback with research and engineering teams
- Create internal playbooks from successful customer projects
Requirements
- Machine learning
- deep learning
- transformer
- mixture of experts
- multimodal model
- sequence model
- scaling law
- training dynamics
- large-model training
- model fine-tuning
- Python
- PyTorch
- distributed training
- distributed data processing
- data curation
- communication