Senior Research Engineer - ML Systems
Confirmed on the employer's own hiring board on Sep 28, 2026. First seen by Alion on Aug 25, 2026.
Senior Research Engineer - ML Systems
Employment Type: Full-time
Company: Permute (www.permute.ai)
Overview
Permute is seeking a Senior Research Engineer to productionize, optimize, and extend the model systems that power AI reasoning over structured data. This role is for builders who can move from research ideas to reliable production systems, including the profiling, testing, and failure handling that prototypes often skip.
We care as much about how you think and build as we do about your background. The ideal candidate can implement research, diagnose model and systems performance, write clean production code, and make sound architectural decisions in a fast-moving startup environment.
Responsibilities
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
Required Qualifications
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