Top Hybrid Machine Learning Engineer Jobs

Develop and optimize machine learning models for document understanding, handle production ML systems, and integrate innovations into APIs.

Design, train, and deploy machine learning models for detecting malicious content, working with large datasets and collaborating with teams to enhance system scalability.

The Staff Software Engineer will develop and deploy high-performance machine learning systems, optimizing large language models for production environments. Responsibilities include ensuring high availability and low latency for AI applications and working collaboratively across teams.

As a Machine Learning Engineer, you'll build and enhance features for a search stack, design ML/NLP solutions, and implement an experimentation framework while ensuring code quality.

As a Senior Machine Learning Engineer, you will own the ML stack, develop generation systems, improve quality, collaborate cross-functionally, and build data pipelines.

Seeking an AI/ML Engineer to own the LLM lifecycle, optimize RAG systems, and collaborate on AI strategy in a hybrid team.

The Software Engineer will lead the development of Baseten Voice AI, focusing on real-time model serving systems, collaboratng with teams on architecture and implementation.

Design and implement 3D machine learning models, process reality capture data, and integrate AI solutions in production environments for robotics applications.

The Machine Learning Researcher will develop new architectures for data-efficient model induction, focusing on optimizing generalization performance and runtime inference in networks, while contributing to a performance-driven code base.

The Applied ML Engineer will design, train, and evaluate machine learning models, optimize inference pipelines, and collaborate with cross-functional teams to deploy AI solutions.