Senior Research Engineer - ML Systems

Posted 2 Days Ago
Chicago, IL, USA
In-Office
150K-250K Annually
Senior level
Artificial Intelligence • Software • Analytics • Business Intelligence
The Role
Productionize and optimize ML systems for AI reasoning over structured data. Responsibilities include improving training and inference performance, porting workloads from CPU to GPU, building training and deployment infrastructure, developing experimentation and observability tools, and implementing new model architectures and fine-tuning methods using Python and PyTorch. The role requires strong ML research, systems engineering, software architecture, optimization, and experimental design skills.
Summary Generated by Built In

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

Skills Required

  • 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
  • Degree in Mathematics, Physics, Computer Science, or a related technical field
  • Experience with end-to-end production ML systems
  • Experience with model training, MLOps, evaluation, and deployment
  • Experience with performance engineering, including CUDA, Triton, quantization, or model compilation
  • Experience with transformers, fine-tuning, post-training, or reinforcement learning
  • Meaningful contributions to open-source ML frameworks or model implementations
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The Company
9 Employees
Year Founded: 2025

What We Do

Permute is an agentic business-intelligence platform that connects, cleans, and reconciles ERP, CRM, finance, spreadsheet, and operating data. It converts fragmented business systems into trusted reporting, dashboards, alerts, and plain-language analysis, helping operators obtain a unified operating view without building a large data team. The company combines data infrastructure, autonomous AI agents, and strategy-consulting capabilities for organizations including financial services and private equity.

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