Top Remote Senior Level Machine Learning Engineer Jobs in Austin, TX

As a Senior Machine Learning Engineer, you will develop machine learning models, automate data pipelines, and collaborate with teams to meet customer needs.

Lead development of AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, ensure responsible AI compliance, produce documentation/patents/publications, and collaborate on MLOps pipelines and stakeholder integrations.

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.

As a Staff AI Software Engineer, you'll develop and optimize scalable AI applications, manage deployment pipelines, and collaborate with cross-functional teams to drive innovative solutions.

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.

Lead the SDL and Cash Line ML teams, developing innovative machine learning models to improve lending solutions and expand credit access. Collaborate with cross-functional teams to drive product strategy and enhance model performance, while mentoring a team of ML scientists.

Lead end-to-end data science projects: build, deploy, and monitor ML models (credit risk, pricing, collections, fraud). Partner cross-functionally, productionize pipelines, write production-grade code, and communicate results to technical and non-technical stakeholders.

Lead AI/machine learning innovations and advanced analytics for new vehicles. Develop statistical models for pricing and valuation, guiding experiments and communicating insights.

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.

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