Machine Learning Jobs in Oakland, CA

As a Research Engineer for ML Systems, you'll optimize GPU clusters for AI, develop advanced technologies for consumer AI models, and improve model training and inference performance.

The role involves supporting ML infrastructure, building diagnostic tools, managing deployments, and optimizing GPU utilization for ML projects.

The Director of Machine Learning will lead the development of ML models for audio, vision, and language applications, mentor the team, ensure project milestones are met, and collaborate with different stakeholders.

Design and maintain ML code for LLM safety, implement techniques for robust evaluation, and collaborate with product and engineering teams.

The role involves owning an LLM safety domain, generating synthetic data, training LLMs, conducting benchmarking, and developing techniques for safer models. Co-author research papers with the team.

Develop and optimize ML algorithms for fraud detection, risk modeling, and personalized experiences by leveraging large datasets and modern modeling techniques. Collaborate with engineering teams for deployment and monitoring.

Lead the design and development of ML infrastructure for autonomous driving, collaborating with ML teams and mentoring engineers in the process.

The role involves shaping the CV strategy, building ML solutions in production, and integrating them with software systems, focusing on computer vision applications.

Lead the design and optimization of advanced AI/ML systems at GEICO, mentoring teams, and ensuring architectural excellence while integrating innovative ML solutions.

This role focuses on optimizing neural model architectures and systems for high performance across multiple GPU/TPU platforms, collaborating with various teams to enhance model efficiency and performance under real-time constraints.

Manage and mentor a team of machine learning engineers, oversee ML project development, collaborate with product management, and drive product milestones.

As a Machine Learning Engineer at Hive, you will prototype, train, and deploy deep learning models, analyze results, and collaborate with cross-functional teams to implement machine learning solutions in production.

The Staff Machine Learning Engineer will implement ML models, design neural networks, maintain scalable code, and lead cross-functional collaborations in AI development.

The Senior Machine Learning Engineer will design, implement, and deploy ML models, contribute to engineering standards, and mentor junior engineers.

Lead the development of cutting-edge AI services and platforms for machine learning, ensuring operational readiness and driving innovation in ML workflows.

The Senior Machine Learning Engineer will develop ML infrastructure, deploy ML models, monitor performance, and mentor junior engineers. Collaboration with cross-functional teams is essential.

The Machine Learning Engineer will develop models to detect harmful behaviors in AI systems, enhance detection systems, and analyze user reports proactively.

The role involves optimizing and productionizing GPU model inference pipelines, building scalable frameworks for Liquid's AI models, and addressing performance issues in multi-GPU settings.

This internship requires pursuing a degree in relevant fields and strong knowledge of machine learning concepts and programming skills in languages like Python, R, or Julia.

The Machine Learning Engineer will build production-grade NLP models for data privacy, manage the ML lifecycle, and collaborate across teams.

As a Senior Solutions Engineer, you will support customers in deploying ML applications, ensuring technical success, and enhancing their understanding of Flyte and Union Cloud.

Design, build, and deploy machine learning solutions. Collaborate with teams to transform research into practical applications using large datasets.

As a Machine Learning Engineer, deploy advanced models, collaborate with teams, optimize data pipelines, and ensure AI applications impact businesses positively.

Drive cross-team engineering efforts to improve user experience on Homefeed by implementing large-scale machine learning systems and enhancing data processing efficiency.

Develop Cloud Data Infrastructure for Assured AI in autonomous vehicles, focusing on ML model training, evaluation, and deployment.