Machine Learning Jobs in Oakland, CA

Develop deep learning models for autonomy applications, implement ML algorithms for self-driving vehicles, and resolve real-world testing issues.

As a Senior Machine Learning Engineer at Roblox, you will develop and implement ML models, optimize architecture for low compute costs, and enhance model performance across various devices.

Develop and optimize machine learning and AI models for financial solutions, conduct experiments, and lead initiatives in AI/GenAI/ML within a collaborative environment.

This role involves building and optimizing ML models for underwriting, managing data pipelines, and integrating various data sources while ensuring low-latency services.

The Software Engineer, MLOps will build cloud infrastructure, develop ML tooling, optimize workloads, and enhance CI/CD pipelines while supporting ML-focused projects.

Design, build, and productionize ML models; maintain data pipelines; collaborate with teams to deliver AI features and enhance product experiences.

As a Machine Learning Engineer, you'll focus on scaling systems for video understanding, refine model deployment, and improve ML infrastructures.

Looking for a Machine Learning Engineer with 2-5 years of experience to train and deploy models for parsing unstructured data, build data pipelines, and enhance LLM accuracy.

As a Machine Learning Scientist, develop ML models focusing on NLP, Speech, and Computer Vision while working on various aspects of the ML stack.

Develop technologies for optimizing AI models in autonomous driving. Collaborate on parallel algorithms and influence compute platform architecture.

The Applied AI/ML Engineer will design, implement, and scale models for urban simulations, collaborating with teams to ensure alignment with customer needs and product requirements.

As a Machine Learning Infrastructure Engineer, you will build and maintain infrastructure for ML model training, optimize deployment, collaborate across teams, and improve internal tools for ML workflows.

Lead a cross-functional team to develop and implement AI products, focusing on Large Language Models and enhancing client solutions.

As a Software Engineer in Applied ML, you'll design solutions for enterprise AI, focusing on product lifecycle and large language models.

Lead the architecture and development of AI and ML features, design evaluation frameworks, and mentor team members to enhance ML-driven product experiences.

The role involves developing AI-powered solutions for e-commerce, optimizing data pipelines, and deploying ML systems at scale while using technologies like React, TypeScript, and AWS.

This role involves designing and optimizing fraud detection models, integrating AI components, and collaborating across teams for large-scale payment systems.

As a Machine Learning Engineer, you will enhance video solutions, scale AI/ML systems, and improve model performance. You'll work with experimentation, deployment, and technical optimization.

Build and design machine learning infrastructure for data scientists, optimize resources, deploy models, and monitor MLOps within a satellite data context.

Interns will work on AI/ML research in sleep fitness, building prototypes, validating data, and collaborating with a cross-functional team. Expect hands-on mentorship and opportunities for publication.

Lead the Machine Learning Platform team at Discord, focusing on building and maintaining ML infrastructure and tools to enhance user experiences.

The role involves optimizing machine learning approaches for 3D simulation, collaborating with diverse teams, and building scalable ML pipelines.

The ML Engineer will design and automate evaluation pipelines, curate datasets, deploy voice models, and ensure model performance while scaling workloads.

The Machine Learning Engineer will develop machine learning models, build data pipelines, and collaborate with teams to identify customer needs. Responsibilities include training and validating deep learning models and presenting performance results.

The role involves designing and maintaining ML infrastructure, solving complex technical problems, and collaborating with engineers and data scientists.