Applied AI ML Scientist

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