Top Hybrid Machine Learning Engineer Jobs

Lead the design and development of Datadog's experimentation engine, translating complex statistical methods into production systems while collaborating with cross-functional teams and educating stakeholders on best practices.

As an Applied Machine Learning Engineer, you will develop AI and ML models to improve healthcare outcomes. Collaborate with teams to implement and optimize machine learning techniques and algorithms.

Develop and deploy state-of-the-art reinforcement learning algorithms for dynamic locomotion and manipulation on humanoid robots. Drive simulation-to-hardware transfer, build and optimize distributed training pipelines, mentor junior engineers, collaborate with hardware teams to debug system-level issues, and convert human demonstration data into robust reference trajectories.

Manage financial markets risk portfolio by applying statistical and machine learning models to optimize forecasting and decision-making. Collaborate with cross-functional teams to provide actionable insights.

Develop ML force fields for materials science, manage data sets, extend ML field accuracy, and collaborate within an interdisciplinary team.

Develop scalable AI/ML software solutions, ensure quality and compliance, optimize performance, and collaborate on real-time systems. Manage data pipelines and models with a firm grasp of best practices.

The Sr. Applied Scientist will develop machine learning models to enhance NBA 2K's gameplay, focusing on matchmaking, user personalization, and in-game economies while collaborating with cross-functional teams.

The Staff Machine Learning Engineer will architect and optimize data infrastructure for multimodal AI models in oncology, focusing on data management and processing pipelines.

The Senior Machine Learning Engineer will enhance AI capabilities for defense applications, collaborate on ML models, optimize data processing, and ensure security in high-stakes environments.

The role involves developing machine learning algorithms for cybersecurity, leading design of security data solutions, and managing security automation tools. Requires strong collaboration skills and a minimum of 5 years in ML or data science.