Top Machine Learning Engineer Jobs in Austin, TX

As an Applied Machine Learning Engineer, you'll design and deploy AI applications, optimize models, and collaborate on integration into products. You will also evaluate model performance and manage AI solutions.

Lead the design and deployment of machine learning systems at Senseye, focusing on computer vision and time-series analysis for mental health diagnostics. Manage a team of ML engineers, establish best practices, and collaborate cross-functionally to align ML outputs with product and regulatory goals.

Implement and optimize production-ready analytics code, maintain Python repositories, research data sources, build predictive models, and collaborate with teams.

As a Principal ML Architect, lead technical strategy and client engagements, mentor ML engineers, and oversee project lifecycle in delivering complex AI systems.

The role involves developing advanced analytical models, supporting model maintenance, and ensuring model governance while collaborating with various stakeholders.

Lead a team of ML Engineers to develop technology for healthcare, emphasizing hiring talent, technical leadership, and collaboration with stakeholders.

As a Staff Applied Scientist, you will develop and deploy advanced credit and risk models, leveraging machine learning to optimize lending decisions. Collaborate with Product, Engineering, and Analytics teams to enhance OnePay's credit access and ensure responsible AI practices.

The role involves designing, developing, and deploying ML models, collaborating with cross-functional teams, and improving model performance through testing and experimentation.

Design and implement machine learning perception systems for autonomous ground vehicles. Work on deep learning models, optimize for edge devices, and collaborate across teams.

The Applied Machine Learning Engineer will develop AI-driven solutions, focusing on backend systems, AI integration, model deployment, and MLOps best practices, while collaborating with cross-functional teams.

Lead data-driven decisions in weather and climate analytics by developing machine learning models, managing projects, and mentoring team members.