Machine Learning Jobs in Berkeley, CA

The role involves collaborating with engineers to deploy ML models, maintaining CI/CD pipelines, implementing model governance, troubleshooting deployments, and staying updated with ML Ops trends.

Lead the design and implementation of Risk machine learning models, optimizing decision-making and enhancing customer experience through data-driven insights.

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

Design and develop auto-labeling algorithms for autonomous driving, collaborating with ML engineers to enhance data quality and efficiency in model training.

Responsible for improving Zoox's HPC infrastructure, optimizing cloud/storage systems for ML workloads, designing APIs, and investigating distributed paradigms.

The Machine Learning Scientist will design experiments, analyze AI model behaviors, and develop metrics to improve model evaluation, collaborating closely with engineers and product teams.

Lead open-source research in AI, design experiments, develop methodologies, and analyze data to enhance model evaluation and transparency.

Lead the design and development of scalable, high-performance real-time data and API infrastructure to support AI applications. Mentor engineers, ensure system reliability, and collaborate across teams.

Develop and implement machine learning models for evaluating disputes and chargebacks, and create a model orchestration layer. Collaborate on data use for enhanced user experiences.

The Machine Learning Engineer will prototype and evaluate LLM features, collaborate with teams, contribute to technical design, and measure success metrics.

The Machine Learning Engineer will develop and deploy ML solutions, analyze data for insights, and monitor model performance to enhance products.

Design and deploy algorithms for gesture detection on embedded hardware, owning the development cycle from design to optimization, while collaborating across teams.

The Lead Machine Learning Engineer will develop, optimize, and deploy machine learning models while collaborating on AI-capable solutions for edge and cloud applications.

Lead a team of Machine Learning Research Scientists to advance deep learning and LLM integration in adtech solutions. Responsibilities include overseeing model design, enhancing algorithms, mentoring staff, and maintaining active participation in the development process.

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

As an AI Engineer at Quintess AI, you will build and deploy AI agents, enhance voice AI capabilities, and collaborate with customers to solve real problems.

As a Research Engineer, you will research human-centered AI mechanisms, evaluate alignment, and enhance Human-AI interactions through data-driven systems.

The ML Engineer will develop voice interfaces, handle ML inference architecture, and enhance speech models for user personalization. Responsibilities include prototyping features and optimizing systems for high-volume requests.

As an ML Scientist at Wispr, you'll develop advanced voice interfaces, focusing on ML personalization and enhancing speech models using fine-tuning and reinforcement learning techniques.