Top Machine Learning Engineer Jobs

You will implement Neural Rendering and generative models for autonomous driving, manage projects, and mentor team members in a cloud-based setting.

Lead and drive strategy for generative AI projects, develop cutting-edge models, engineer data pipelines, and collaborate across teams to implement AI solutions.

Lead the Generative AI team at Xometry, setting vision and strategy, developing multimodal models, and collaborating with various stakeholders to deliver AI solutions.

Lead the Generative AI team by setting technical direction, developing large-scale models, and mentoring team members while collaborating across departments.

Lead the Generative AI team by setting technical direction, developing models, creating data pipelines, and driving AI strategy across Xometry's marketplace.

Design and implement AI-driven features for claims synthesis, leveraging LLMs and statistical techniques, while ensuring system performance and accuracy.

As a Staff Machine Learning Engineer, you'll design large-scale recommendation and retrieval systems, enhance quizlet's personalization through ML, collaborate with teams, and ensure models align with user outcomes.

Design and implement deep learning algorithms for autonomous driving, optimizing neural networks for real-time performance, and integrating multimodal data.

As a Staff Machine Learning Engineer, you will design and evaluate ML models, optimize performance, and work with stakeholders to deliver AI solutions.

As a Senior Software Engineer, you'll enhance servicing decisions with ML models, improve experimentation, and scale decisioning systems for personalized borrower experiences.

Lead technical projects for the Workers AI team focused on deploying AI inference, building innovative features, and enhancing developer experience.

Design and scale infrastructure for machine learning models, build pipelines, and optimize for performance in a collaborative remote environment.

This role involves building and optimizing ML systems and frameworks, collaborating with teams to enhance research and development in ML models.

The role involves designing and maintaining ML infrastructure, solving complex technical problems, and collaborating with engineers and data scientists.

The Machine Learning Engineer will manage the end-to-end ML lifecycle, collaborate with cross-functional teams, develop production models, and monitor their performance in high-stakes environments.

Lead the development of device intelligence and fingerprinting systems by designing backend services, collaborating on integrations, and applying advanced machine learning algorithms to enhance security and combat fraud.

The Machine Learning Engineer will design, develop, and deploy ML models using Google Cloud tools, focusing on Document AI and Vertex AI, ensuring scalable applications and efficient performance.

The Staff Machine Learning Engineer will lead ML strategy, architect AI infrastructure, mentor engineers, and develop impactful AI systems.

This position involves developing and deploying AI and machine learning solutions, contributing to cross-functional projects, and mentoring junior staff, with a focus on healthcare and insurance applications.

The Lead AI & Machine Learning Engineer will develop and industrialize GenAI and machine learning solutions, lead projects, mentor junior members, and improve current ML models within a SaaS framework.

As a Senior Open-Source ML Engineer, you will enhance computer vision tools, optimize models for production, mentor others, and collaborate with the community to democratize AI.

The Machine Learning Engineer will build and scale ML infrastructure, drive model performance, and ensure reliability while collaborating with various teams to optimize feature engineering pipelines and champion best practices.

The AI/ML Engineer will design and maintain scalable ML pipelines, collaborate with data scientists, implement containerized workloads, develop APIs, and monitor ML systems.

Design and implement end-to-end AI agent systems, collaborate with cross-functional teams, and oversee deployment and performance evaluation in production environments.

Design and implement AI-powered solutions for document extraction and financial reasoning, focusing on ML pipelines and application of LLMs.