Job Responsibilities

• Advanced post-training of large language models (e.g. SFT, RLHF/RLAIF, continual pretraining).
• Aligning models for reliable JSON-schema function calls and external tool usage.
• Design, deploy, and operate Model Context Protocol (MCP) servers that handle checkpoint routing, manage context windows, and enforce safety gates.
• Experience in distributed training and inference with DeepSpeed/FSDP, LoRA/QLoRA, mixed precision, and performance tuning on vLLM or Triton clusters.
• Build offline and live eval pipelines for alignment, factuality, grounding, and hallucinations.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• 3+ years of experience in developing and optimizing large language models.
• Proven track record in implementing advanced post-training techniques (SFT, RLHF, RLAIF, continual pretraining).
• Hands-on experience with distributed training frameworks (DeepSpeed, FSDP) and optimization techniques (LoRA, QLoRA, mixed precision).
• Familiarity with model alignment, JSON-schema function calls, and external tool integration.
• Experience in building and maintaining evaluation pipelines for model performance assessment.
• Proficiency in Python and relevant machine learning frameworks (e.g., PyTorch, TensorFlow).
• Strong understanding of distributed systems and high-performance computing.
• Experience with model deployment and inference optimization on vLLM or Triton clusters.
• Knowledge of JSON-schema and API development.