Top Remote Machine Learning Jobs

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

The Senior ML Engineer will automate component placement on PCBs, working on R&D and production using ML and optimization techniques.

Collaborate to scale self-driving systems, build training frameworks, profile model performance, and evaluate emerging technologies for training and inference.

As a Senior ML Engineer, you will design, build, and deploy ML models, work with MLOps tools, and collaborate within a cross-functional team to enhance shop management software capabilities.

This role involves designing, training, and evaluating ML models, collaborating on AI initiatives, and optimizing model performance for practical applications.

As a Senior Machine Learning Engineer (Tech Lead), you'll lead the ML technical direction, design architectures for loco-manipulation, mentor team members, and manage cross-functional partnerships while writing production-quality code.

The Senior Machine Learning Engineer will lead the development of ML models to optimize clinical trial workflows, enhance patient engagement, and improve trial efficiency through collaboration and technical leadership.

Lead cutting-edge machine learning research, develop algorithms, and deploy production-ready models in a healthcare-focused AI environment. Collaborate with engineers and clinicians to optimize healthcare systems.

Design and deploy machine learning models for personalized recommendations and search on Zillow's platforms. Collaborate with cross-functional teams to enhance user experience.

The Staff Machine Learning Engineer will lead the development of advanced AI models, collaborate with teams, conduct research, and mentor junior engineers.

As a Senior ML Engineer, you'll own ML-driven features from modeling to deployment, drive innovation in generative AI, and mentor junior members.

The Staff Machine Learning Engineer will enhance Quora's recommendation systems, lead machine learning projects, and own end-to-end ML processes.

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 ML Engineer at Torc, you will develop and maintain ML frameworks, enabling advanced solutions for autonomous vehicles and improving ML platforms.

Contribute to developing machine learning models for multi-scale biology, focusing on design, deployment and optimization across teams and technologies.

As a Staff Machine Learning Engineer, you will enhance recommendation systems using ML, collaborate with teams, and refine reward signals for user satisfaction.

The role involves leading ML strategy, architecting systems, designing algorithms, collaborating with teams, and mentoring engineers in AI for enterprise analytics.

The AI & Machine Learning Director will lead client engagements, analyze corporate performance, and drive improvements in profitability through machine learning and data analysis, while also managing business development and team collaboration.

The role involves improving recommendation systems using machine learning, developing end-to-end ML systems, and collaborating with engineers.

As a Machine Learning Engineer, you'll build and optimize ML systems for music promotion, ensuring they are efficient and scalable while collaborating with cross-functional teams.

The Founding ML Engineer will develop and deploy ML systems, collaborate with teams, and translate ambiguous problems into production ML solutions, shifting between client delivery and platform development.

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

The Principal Applied ML Researcher leads the ML strategy for a platform, focusing on applied ML systems and agent workflows to ensure reliable execution in regulated environments. They drive innovations, oversee the development lifecycle, and lead the integration of LLMs into production systems while ensuring compliance and performance metrics are met.

As a Machine Learning Engineer, you will leverage LLMs to improve language understanding, design ML models, and collaborate on innovative features.

The Machine Learning Solutions Architect designs and implements data solutions, overseeing model deployment and ensuring robust performance and integration within customer environments.