Top Machine Learning Engineer Jobs in Austin, TX

Design and deploy machine learning models for personalized recommendations and search on Zillow's platforms. Collaborate with cross-functional teams to enhance user experience.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

As a Staff Software Engineer, you will lead the development of Cake's AI platform infrastructure, focusing on building, operating, and improving ML systems. The role requires collaboration with customers to address ML challenges and enhance platform capabilities, emphasizing ownership and operational excellence.

Seeking a Founding ML Engineer to design and deploy LTV prediction systems, handle MLOps, and collaborate with product teams. Requires strong ML experience and knowledge of cloud environments and data stacks.

The Principal AI/ML Platform Engineer leads the development and delivery of ML and GenAI platforms, ensuring scalability, compliance, and performance across various workflows and products, while mentoring the engineering team.

As a Machine Learning Engineer, you'll build AI solutions from structured leadership data, develop RAG-based agents, and enhance the Leadership Intelligence Platform, focusing on efficiency and collaboration with research partners.

As a Senior Machine Learning Engineer, you'll lead ML solution development, drive advanced statistical modeling, collaborate with stakeholders, and ensure scalable systems meet business needs.

The Lead AI/ML Engineer evaluates emerging technologies, leads teams in adopting solutions, and provides technical leadership while mentoring junior specialists.

The Research Engineer will improve dataset quality for self-driving systems by designing algorithms for auto-labeling and data monitoring, collaborating with teams, and enhancing model performance.

The role involves designing, building, and optimizing large-scale ML systems for personalized recommendations, focusing on data ingestion, model training, and collaboration with cross-functional teams.

This role involves developing and integrating AI/ML models within GCP using Python, optimizing LLM performance, and applying MLOps practices. Strong collaboration with architects is required for implementation and analysis of workflows.

The Staff ML Ops Engineer builds and maintains infrastructure for ML applications, ensuring they are robust and production-ready, while collaborating with data scientists and ML engineers.

The role involves developing scalable ML solutions, leading cross-functional discussions, analyzing knowledge data, and producing technical documentation to improve search relevance using AI and machine learning technologies.

Lead the development of device intelligence and fingerprinting systems by designing backend services, collaborating on integrations, and applying advanced machine learning algorithms to enhance security and combat fraud.

The role involves designing and developing machine learning infrastructure and models, consulting on best practices, and building scalable services for AI integration.

The Machine Learning Engineer will design, develop, and deploy ML models using Google Cloud tools, focusing on Document AI and Vertex AI, ensuring scalable applications and efficient performance.

The Principal Machine Learning Engineer will lead the design and implementation of ML models and data architectures for HelloData, ensuring production readiness and system scalability while collaborating with product teams.

The Sr. ML Engineer will enhance Visa's AI platform across various environments, optimize code, develop solutions, and build high-performance systems using cloud technologies and machine learning techniques.

Lead and manage a team developing ML models for self-driving vehicles, define motion planning projects, and collaborate with cross-functional teams to enhance performance.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

The Principal Machine Learning Engineer will lead ML lifecycle projects, developing AI strategies, and implementing advanced machine learning solutions for government clients, while ensuring data integrity and operational efficiency.

The Senior Software Engineer will design and build infrastructure for ML model training and deployment, focusing on AWS technologies, reliability, and collaboration with ML teams.

The Senior Machine Learning Engineer at SmarterDx will deploy, maintain, and optimize machine learning models, collaborating across disciplines to integrate these solutions and improve clinical AI outcomes.

As a Machine Learning Customer Success Engineer, you will facilitate ML workflows, manage user relationships, and contribute to FiftyOne's development.