Applied Machine Learning Engineer

Build and improve the core ML systems powering Inference.net's custom model training platform. Lead projects from data intake through trained models, develop infrastructure and tooling, optimize model quality and efficiency, and apply research techniques in production while working directly with customers.

Responsibilities

• Lead projects from data intake through the full training pipeline, including processing, cleaning, and preparing datasets for model training
• Build 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 internal frameworks and iterate based on evaluation results
• Develop robust benchmarks and evaluation frameworks for custom models
• Build systems to automate portions of the training workflow
• Take research features and ship them into production settings
• Apply SFT, RL, and model optimization techniques to improve training quality and efficiency
• Collaborate with infrastructure engineers to scale training across the GPU fleet
• 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 are trained
• Experience with LLM-specific training frameworks such as Hugging Face Transformers, DeepSpeed, or Axolotl
• Experience training on NVIDIA GPUs
• Strong data processing skills and experience building ETL pipelines with large datasets
• Track record of creating benchmarks and evaluations
• Ability to apply research techniques to production systems

Benefits

• Equity
• Comprehensive benefits
• Large compute budget or GPU reservation
• Technical support and autonomy