qdrant jobs in San Diego

Design, develop, and optimize machine learning systems for production AI platforms, focusing on model development, inference optimization, and scalable ML infrastructure. Responsibilities include building ML pipelines and frameworks, optimizing model inference for latency and cost, integrating LLMs into APIs and microservices, and designing data pipelines for preprocessing and feature engineering.…See more The role requires strong software engineering fundamentals combined with deep ML expertise, working across PyTorch/TensorFlow, model-serving systems, distributed computing, and GPU environments.

Design, develop, and optimize production machine learning systems including model development, inference optimization, and scalable ML infrastructure. Build end-to-end ML pipelines from training through deployment, optimize model inference for latency and cost, integrate LLM/ML models into APIs and microservices, and engineer data pipelines for preprocessing and validation.…See more Requires strong software engineering fundamentals with deep ML expertise, proficiency in Python and systems languages, and experience with ML frameworks, model serving, and distributed computing.

Design, develop, and optimize machine learning systems and models for production AI platforms, with focus on inference optimization, scalable ML infrastructure, and deployment. Responsibilities include building ML pipelines, optimizing model inference across hardware environments, integrating LLMs and models into APIs and microservices, designing data pipelines for ingestion and feature engineering, and collaborating cross-functionally on end-to-end ML solutions.…See more Requires strong software engineering fundamentals combined with deep ML expertise, proficiency in Python and at least one systems language (C++, Rust, or Go), and solid understanding of ML frameworks, transformer architectures, and model deployment systems.

Design, develop, and optimize machine learning systems for production AI platforms, including model development, inference optimization, and scalable ML infrastructure. Build training-to-deployment pipelines, optimize model serving for latency and cost, and integrate LLMs and generative AI models into microservices and APIs.…See more Develop data pipelines for ingestion, preprocessing, and feature engineering while collaborating cross-functionally with product, platform, and hardware teams to deliver end-to-end ML solutions.