Platform Engineer, Model Shaping - Together AI

Key Responsibilities

• Design and implement high‑performance backend services that enable developers to select, fine‑tune, and evaluate large language models for specific downstream tasks.
• Develop and maintain pipelines for efficient model training, data ingestion, and continuous evaluation using PyTorch and related ML frameworks.
• Build and operate cloud‑native infrastructure (Docker, Kubernetes, AWS) to support scalable, reproducible experiments and production deployments.
• Collaborate with research scientists to prototype novel training and evaluation methods, translating research prototypes into production‑ready code.
• Implement monitoring, logging, and automated testing to ensure reliability and observability of model‑shaping services.

Requirements

• Strong proficiency in Python and experience with deep learning libraries such as PyTorch or TensorFlow.
• Hands‑on experience building and operating MLOps pipelines, containerization (Docker) and orchestration (Kubernetes) in cloud environments.
• Solid understanding of machine learning concepts, especially model fine‑tuning, evaluation metrics, and data preprocessing for large language models.
• Ability to write clean, maintainable code and contribute to shared codebases using version control (Git).
• Excellent problem‑solving skills and a collaborative mindset for working with cross‑functional research and engineering teams.