Applied Machine Learning Engineer (LLMs & RL)
Core
Design and implement post-training pipelines for large language models (LLMs), develop reinforcement learning environments and reward models, and conduct training runs to improve model capabilities for agentic applications.
Role type
Applied Machine Learning Engineer (LLMs & RL)
Builds
Post-training pipelines, RL environments, reward models, and evaluation frameworks for LLMs
Domain
Artificial Intelligence / Large Language Models / Reinforcement Learning
Deliverable
production ML models
Required skills
Python, C++, LLM architectures, optimization, model training fine tuning, distributed training, debugging, research experiment design, ablation studies, benchmarking, evaluation metrics
Preferred skills
Masters or PhD degrees, full post-training pipeline implementation (SFT & RL), evaluation framework design, modeling distillation, quantization
Technologies
Python, C++
