Top Remote Senior Level Machine Learning Jobs in San Francisco Bay Area, CA

As a Machine Learning Engineer, you will maintain accurate financial records, analyze data, prepare reports, ensure compliance, and support budgeting and audits. You will play a key role in process improvements and communicate financial information effectively across teams.

Design and build ML architectures and data pipelines for large-scale data repositories. Work on Clari's AI models and platform. Collaborate with stakeholders to deliver enterprise products. Fully remote opportunity in the United States.

Design and develop scalable platforms and services focused on driving business impact using machine learning. Define ML roadmap, make architectural decisions, and contribute to AI strategy. Maintain scalable ML data pipelines, optimize deep learning models, and drive revenue growth through high-value product attributes. Promote software engineering best practices and automate workflows. Deploy, monitor, and improve models in production environments. Proficiency in Python, PyTorch, Kubernetes, RDBMS, and cloud-native application development is critical.

Develop a scalable AI platform to automate tasks and enhance developer productivity at Block. Collaborate with teams to implement AI solutions and drive AI innovation. Serve as an expert in AI technology and guide the company in AI strategy implementation.

The Senior Machine Learning Engineer will lead projects in automating and optimizing Square product features using conversational and generative machine learning techniques. They will collaborate with business leaders, develop new ML & data products, and drive end-to-end cross-functional machine learning projects.

Design equitable and competitive compensation programs. Base pay ranges from $165,500 to $265,800. Additional benefits such as bonuses, commissions, and equity may be provided.

As a Senior Software Engineer on the ML Platform team, you'll build and maintain software applications for machine learning model deployment and process automation. The role includes developing tooling and infrastructure for research scientists, enabling rapid model experimentation, and ensuring automated, repeatable processes for model training and deployment.