Research Engineers, Post-Training
Core
Design and run post-training workflows to improve the behavior, reliability, and usefulness of AI systems in production environments.
Role type
Senior Research Engineer (Post-Training)
Builds
Production-ready AI systems, evaluation frameworks, and experimentation infrastructure for enterprise customers.
Domain
Applied AI, Large Language Models (LLMs), Enterprise Operations
Deliverable
production ML models
Required skills
fine-tuning, preference optimization, reinforcement learning, reward modeling, synthetic data generation, evaluation suite design, pipeline development, controlled experimentation, behavioral analysis, infrastructure building
Preferred skills
AI-native working style, bias towards measurement, ownership mentality, applied constraints management
Technologies
Python, PyTorch, Hugging Face, LangChain, vector databases, cloud infrastructure (AWS/GCP/Azure)
Responsibilities
Design and run post-training workflows; Develop datasets, preference signals, and evaluation suites; Investigate post-training techniques across enterprise workflows; Build infrastructure for experimentation and regression testing; Partner with researchers and engineers to apply techniques; Analyze model outputs and production traces to identify improvements; Create repeatable processes for adapting AI systems to customer domains.
Seniority
Senior, hands-on IC