Applied Machine Learning Engineer

About the role

You will be responsible for building and improving the core ML systems that power our custom model training platform, while also applying these systems directly for customers. Your role sits at the intersection of applied research and production engineering. You'll lead projects from data intake to trained model, building the infrastructure and tooling along the way.

Responsibilities

• Lead projects from from data intake through the full training pipeline, including processing, cleaning, and preparing datasets for model training
• Create and maintain data processing pipelines for aggregating, transforming, and validating training data
• Create dashboards and visualization tools to display training metrics, data quality, and model performance
• Train models using our internal frameworks and iterate based on evaluation results
• Develop robust benchmarks and evaluation frameworks that ensure custom models match or exceed frontier performance
• Build systems to automate portions of the training workflow, reducing manual intervention and improving consistency
• Apply the latest techniques in SFT, RL, and model optimization to improve training quality and efficiency
• Collaborate with infrastructure engineers to scale training across our GPU fleet
• Deeply understand customer use cases to inform training strategies and surface edge cases

Requirements

• 2+ years of experience training AI models using PyTorch
• Hands-on experience with post-training LLMs using SFT or RL
• Strong understanding of transformer architectures and how they're trained
• Experience with LLM-specific training frameworks (e.g., Hugging Face Transformers, DeepSpeed, Axolotl, or similar)
• Experience training on NVIDIA GPUs
• Strong data processing skills and comfortable building ETL pipelines and working with large datasets
• Track record of creating benchmarks and evaluations
• Able to take research techniques and apply them to production systems

Nice-to-Have Experience

• Experience with model distillation or knowledge transfer
• Experience building dashboards and data visualization tools
• Familiarity with vision encoders and multimodal models
• Experience with distributed training at scale
• Contributions to open-source ML projects