Top Remote Machine Learning Engineer Jobs in Austin, TX

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

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 Staff Machine Learning Engineer will lead the development of AI solutions, ensure project delivery, and manage technical teams while interfacing with customers. Responsibilities include designing data pipelines, overseeing machine learning model development, and facilitating customer engagement.

The Senior Applied Scientist II is responsible for designing and implementing ML models to optimize the supply chain, collaborating across teams, and driving data-driven decision-making.

The role involves building computer vision models, researching deep learning methods, handling large datasets, and implementing MLOps best practices.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

Design, develop, and deliver machine learning models for clinical data. Collaborate with stakeholders and optimize production ML systems.

As a Staff Machine Learning Engineer, you will develop and maintain ML models for decision-making, optimize pipelines, and collaborate with a diverse engineering team.

As a Machine Learning Engineer, you will perform analysis on healthcare data, develop and deploy clinical machine learning models, and collaborate with various stakeholders.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

The Machine Learning Engineer will design, build, and maintain production machine learning systems, focusing on scalability, reliability, and integration with existing products. Responsibilities include developing ML pipelines, establishing ML Ops best practices, and collaborating on product capabilities.

Lead design and implementation of enterprise-grade AI systems, architect AI infrastructure, and drive technical strategy for AI-powered products primarily for military applications.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

Partner with CV researchers to productionize models, optimize training and real-time inference performance, design reusable abstractions, and collaborate with MLOps/backend teams to evolve the CV inference platform for scale and reliability.

Design, build, and scale production ML systems (including LLMs) to automate customer support and compliance workflows. Lead technical roadmaps, write high-quality Python code, mentor engineers, ensure secure, scalable architectures, and resolve complex reliability issues.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

As a Senior Machine Learning Engineer, you will design ML infrastructure, lead zero-to-one initiatives, collaborate on projects, and enhance scalability in the development of ML applications.

The role involves designing, optimizing, and deploying machine learning models for edge environments, focusing on computer vision. The engineer will collaborate with teams to integrate models and improve inference efficiency while mentoring peers and upholding company values.

As a Staff Machine Learning Engineer, optimize card pricing models, lead ML lifecycle, collaborate with experts, and enhance predictive features.

The Machine Learning Engineer will design and deploy computer vision models, build training pipelines, and enhance ML infrastructure to improve agricultural field operations.

Design and implement scalable machine learning pipelines, collaborate with cross-functional teams, ensure deployment standards, and champion best practices in observability and monitoring.