Top Machine Learning Jobs in Houston, TX

Lead the design and implementation of deep learning models for biological data, focusing on model architecture, project leadership, and innovation in machine learning applications in biology.

The Senior Machine Learning Engineer will design and maintain ML solutions for fraud detection, collaborating across teams and establishing MLOps best practices.

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 AI systems for vulnerability detection and penetration testing with expertise in AI/ML technologies and cybersecurity principles.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

Develop AI tools using statistical modeling and establish pipelines for causal inference and predictive analytics to assist political campaigns.

Develop and optimize machine learning models for processing financial documents, mentor junior staff, and collaborate across teams to enhance software products.

The Senior Staff AI/ML Engineer will design, develop, and deploy scalable machine learning models, collaborating with teams to address business challenges and ensure high performance in production environments.

You will take ownership of ML Ops systems, improve CI/CD processes, and enhance software infrastructure for ML Engineers providing real-time intelligence products.

As a Senior Software Engineer in Machine Learning, you'll develop and maintain machine learning models for geospatial analytics, collaborate with teams to refine algorithms, and ensure high-quality deployment of models at scale.

A Machine Learning Engineer will design and optimize AI components, develop datasets, evaluate models, and collaborate cross-functionally to enhance AI insights.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.

Lead the design of AI/ML ecosystem; define architecture standards, mentor teams, and oversee ML platform development focusing on innovative healthcare solutions.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

The Senior ML Engineer will design evaluations for AI features, optimize pipelines, and ensure quality in ML systems while collaborating with cross-functional teams.

The role involves managing data pipelines for 3D generative models, developing infrastructure, collaborating with research teams, and ensuring data quality.

Collaborate to scale self-driving systems, build training frameworks, profile model performance, and evaluate emerging technologies for training and inference.

Lead the design and deployment of scalable ML infrastructure, focusing on MLOps and LLM strategies, while mentoring team members and ensuring operational maturity.

Design and deliver customer experience solutions using machine learning techniques, analyze customer data, implement ML algorithms, and automate processes.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

The Staff Machine Learning Engineer will enhance Quora's recommendation systems, lead machine learning projects, and own end-to-end ML processes.

As a Staff Software Engineer, you'll develop and maintain backend and pipeline systems for search and recommendation services on Reddit, focusing on low-latency, scalable model-serving APIs.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

The AI/ML Engineer will architect, maintain data pipelines, develop AI applications, and optimize distributed systems for fraud detection and prevention.

Implement and optimize production-ready analytics code, maintain Python repositories, research data sources, build predictive models, and collaborate with teams.