Top Remote Machine Learning Jobs in Sacramento, CA

Design and implement ML-driven solutions for user engagement and content discovery. Collaborate across teams to enhance product features using advanced machine learning models.

The role involves designing and maintaining ML infrastructure, ensuring performance and security, collaborating with ML engineers, and implementing modern AI capabilities.

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.

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

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

Lead high-impact machine learning projects, developing systems for advertising optimization, collaborating cross-functionally, and implementing statistical techniques.

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

The role involves developing cloud solutions and enhancing ML/AI pipelines, ensuring service availability, and automating deployments in a scalable environment.

The Sr. Staff Machine Learning Engineer will lead architecture and development of ML systems, enhance patient interactions, optimize treatment recommendations, and mentor engineers.

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

The Machine Learning Engineer will build and scale ML infrastructure, drive model performance, and ensure reliability while collaborating with various teams to optimize feature engineering pipelines and champion best practices.

Responsible for the end-to-end lifecycle of machine learning systems including design, training, deployment, and MLOps best practices implementation.

Lead the integration of AI in CPU/SoC design and verification. Build AI copilots for workflows, partner with EDA vendors, and define methodologies for CPU design improvement.

The Machine Learning Engineer will design, train, and evaluate computer vision models, collaborate on engineering improvements, and deploy ML systems for surgical workflows.

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.

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

As a Lead Machine Learning Engineer, design and maintain scalable ML solutions, optimize workflows, evaluate model performance, and collaborate with cross-functional teams to integrate complex data-driven applications, ensuring high reliability and performance standards.

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.

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

Lead ML Performance Optimization for autonomous driving, collaborating across teams, implementing optimization techniques, and mentoring engineers for career growth.

As a Senior Data Scientist, you will develop models to extract insights from complex datasets, focusing on temporal and geospatial features, while collaborating with engineering teams to implement solutions.

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

Design and implement machine learning models to improve Risk and Fraud detection systems. Lead technical strategy and partner with operations to address rapidly evolving challenges.