Top Machine Learning Engineer Jobs in Austin, TX

Intern will explore AI/ML techniques for engineering data, develop AI-driven approaches, prototype and test AI models, and participate in technical discussions.

Design, train, optimize, and deploy efficient on-device ML models (audio and vision) for resource-constrained products; maintain Ambiq ADKs; port and optimize customer models; apply model-efficiency techniques; produce customer-facing demos, docs, and benchmarks.

The Machine Learning Engineer will develop and optimize models, manage datasets, enhance training pipelines, and collaborate across teams to innovate in autonomous vehicle technology.

Lead and execute applied AI/ML projects: translate ambiguous business problems into technical approaches, architect and validate models and pipelines, engage clients, mentor small teams, ensure code and deliverable quality, and contribute to business development and technical strategy.

Design and implement compiler optimization algorithms for deep learning inference, collaborate with framework and GPU architecture teams, define public APIs, and deliver high-performance AOT and JIT compilation with reduced memory and fast build times.

Lead design, build, and deployment of scalable ML/AI models (including image/vision) to production. Collaborate with engineering and business teams, monitor model quality and bias, and apply data-driven solutions to predict and aggregate risk.

Lead a team of data scientists at Resideo, overseeing machine learning model development and execution for HVAC performance, leak detection, and energy optimization.

The Applied Scientist will develop and enhance machine learning models, design A/B tests, evaluate performance, and collaborate with stakeholders to enhance decision-making and operational workflows in healthcare.

As an intern, you'll assist in developing edge computing solutions, collaborate with machine learning teams, and improve data processing for satellites.

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.