Senior Research Engineer - ML Systems

• Productionize and optimize our existing learned evidence architecture for structured data
• Improve training and inference performance, including throughput, latency, memory use, reliability, and cost
• Port and optimize model training and inference workloads from CPU to GPU
• Build production systems supporting model training, evaluation, deployment, and inference
• Develop tooling for experimentation, reproducibility, monitoring, and observability
• Write clean, maintainable Python and PyTorch systems that integrate with Permute’s broader platform
• Design and evaluate new heads, layers, objectives, and fine-tuning methods
• Explore new model variants, including transformer-based architectures and reinforcement learning
• Collaborate with engineering and product teams to deliver model capabilities that power production AI features

• Strong background in machine learning research and ML systems
• Experience building and training models with PyTorch
• Strong foundation in algorithms, statistics, optimization, and experimental design
• Strong software engineering and system architecture skills
• 5+ years building ML or performance-sensitive software systems

Preferred Background

• Degree in Mathematics, Physics, Computer Science, or a related technical field

Experience with:

• End-to-end production ML systems
• Model training, MLOps, evaluation, and deployment
• Performance engineering, including CUDA, Triton, quantization, or model compilation
• Transformers, fine-tuning, post-training, or reinforcement learning
• Meaningful contributions to open-source ML frameworks or model implementations

Compensation Range: $150K - $250K