Top Machine Learning Jobs in Phoenix, AZ

Lead development of data pipelines and workflows for ML models, design scalable data processing environments, and ensure high data quality.

The Senior ML Engineer will design and deploy large-scale ML systems for personalized recommendations, directly impacting user engagement and revenue. Responsibilities include building models serving 100M+ predictions daily and optimizing data processing pipelines.

As a Senior ML Engineer, you will develop and optimize ad algorithms to enhance engagement and revenue, collaborate with teams, and implement scalable ML systems.

The role involves building and optimizing large-scale ML systems for personalized recommendations that drive engagement, retention, and revenue, impacting millions of users daily.

The role involves building and optimizing machine learning systems for ad performance, collaborating with teams to enhance targeting and revenue. Responsibilities include developing algorithms, conducting experiments, and improving infrastructure for scalable ad decisioning.

As an AI/ML Engineer, you will design, train, and deploy machine learning models, implement MLOps best practices, and collaborate with cross-functional teams to integrate AI capabilities into products.

The Machine Learning Engineer will build and scale ML models, collaborate with cross-functional teams, and improve real-time recommendations for Stitch Fix's clients.

As a Machine Learning Engineer, you will enhance ML algorithms, write production-level code, and collaborate within a distributed engineering team to optimize TV advertising technologies.

As an Associate Machine Learning Engineer, you'll apply foundational AI concepts in a production setting, contributing to model assessments, automated training processes, and collaborative development in a fast-paced Agile environment.

The Applied Scientist will integrate LLMs into production systems, develop NLP and CV models, and lead AI initiatives for wealth.com.

Implement and optimize machine learning features for 3D models, collaborating with research and engineering teams and ensuring reliable deployment processes.

The Software Engineer will develop machine learning infrastructure, optimize models, and create tools for data analysis while collaborating with a diverse team.

Design and implement backend ML systems for personalized feeds at Reddit. Mentor teams, drive technical discussions, and improve MLOps tools.

Develop and support ML infrastructure for large-scale scientific workflows, improve automation processes, and contribute to product features in AI-enabled drug discovery.

Lead the design and development of machine learning infrastructure for robotics, defining strategies, collaborating with teams, and ensuring best practices in MLOps.

The role focuses on building scalable backend systems for ML features, collaborating on model adaptation, ensuring production reliability, and API design.

Design and develop scalable machine learning systems for fraud insights, collaborating with teams to deliver high-quality ML infrastructure effectively.

As a Principal ML Engineer, you will design ML architecture, tackle technical challenges, mentor engineers, and influence ML strategy across the company.

The Senior Data Scientist will develop and deploy machine learning and AI models to enhance business strategies in a B2B SaaS context, collaborating with cross-functional teams to transform data into actionable insights.

Design and implement software solutions for machine learning operations, focusing on geospatial imagery analytics and MLOps software development.

Develop AI-driven features and systems, focusing on LLM fine-tuning and integration with third-party tools. Stay current with AI advancements.

The ML Scientist will lead data science and ML projects, automate reporting, support product development, and maintain ML infrastructure at Harvard Business Publishing.

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.

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.

The Staff Machine Learning Engineer will develop predictive models for financial metrics and work closely with data teams, ensuring collaboration and integration of high-quality models.