Top Software Machine Learning Engineer Jobs in Austin, TX

The Staff AI/ML Engineer will design and implement scalable ML infrastructure solutions, lead projects, mentor peers, and enhance ML productivity at GM.

Lead the development of ML models for search and personalization at Realtor.com, driving innovation and mentoring talent in a collaborative environment.

The Machine Learning Engineer will lead the development of a semantic intelligence platform, building knowledge graphs and AI software to enhance data accessibility and business insights.

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.

As an AI & ML Engineering Intern, you'll design and build internal AI-powered tools, automate workflows, and contribute to shared AI infrastructure, with a focus on LLMs and machine learning frameworks.

The Senior Software Engineer will lead projects in building AI/ML workflows, developing model services, and enhancing APIs, while collaborating across teams to drive technical excellence and operational success.

The Machine Learning Engineer will maintain and expand the ML platform, working on scalable systems, model serving, and collaborating closely with cross-functional teams.

The Senior Machine Learning Engineer will design, implement, and optimize machine learning models, drive AI initiatives, and collaborate cross-functionally to enhance SailPoint's AI capabilities.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

The Engineering Manager, Machine Learning will lead a team focusing on 3D deep learning and computer vision, manage the ML roadmap, and implement best practices for MLOps pipelines.