Applied Machine Learning Engineer

About the Company

Inference.net trains and hosts specialized language models for companies who want frontier-quality AI at a fraction of the cost. We are a well-funded ten-person team of engineers working in-person in downtown San Francisco on difficult, high-impact engineering problems.

Responsibilities

• Lead projects from data intake through the full training pipeline, including processing, cleaning, and preparing datasets
• Build and maintain data processing pipelines for aggregating, transforming, and validating training data
• Create dashboards and visualization tools to display training metrics and model performance
• Train models using internal frameworks and iterate based on evaluation results
• Develop robust benchmarks and evaluation frameworks
• Build systems to automate portions of the training workflow
• Apply the latest techniques in SFT, RL, and model optimization
• Collaborate with infrastructure engineers to scale training across GPU fleets

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
• Experience with LLM-specific training frameworks (e.g., Hugging Face, DeepSpeed, Axolotl)
• Experience training on NVIDIA GPUs
• Strong data processing skills and experience building ETL pipelines
• Track record of creating benchmarks and evaluations
• Ability to apply research techniques to production systems

Preferred Qualifications

• 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