Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Manages customer onboarding for micro and small fleet accounts by processing applications, monitoring automated engagement campaigns, supporting enrollment and activation, validating account data, educating customers, escalating issues, and coordinating with Sales and internal teams. The role emphasizes accurate workflow management, timely transitions, customer service, and early lifecycle success.

Own post-sale relationships with referral partners, driving adoption, retention, renewals, expansion, and revenue growth. Serve as a strategic advisor, lead business reviews, resolve escalations, coordinate cross-functional teams, enable product usage, and advocate for partner needs. Maintain partner records and report on satisfaction, renewal, and revenue metrics while developing success strategies and playbooks.

The Principal AI/ML Engineer will lead the development of AI/ML platforms, drive technology architecture, ensure quality and integration, and provide strategic leadership while managing a team of engineers.

Architect, build, and deploy production-grade AI/ML platforms and LLM/RAG solutions. Lead system design, CI/CD and Terraform-based IaC, ensure reliability and monitoring, mentor engineers, run incident response, and prototype emerging AI technologies.

Leads business intelligence, workflow automation, and data stewardship for financial risk operations. Builds governed Tableau and Power BI reporting environments, develops Python, SQL, RPA, and ETL solutions, analyzes trends, and delivers predictive insights. Maintains audit-ready controls, data integrity, version management, error handling, and compliance. Partners with stakeholders to modernize manual processes, improve efficiency, track operational performance, and scale self-service analytics.

Develop new B2B corporate payments business across North America, focusing on virtual card opportunities and AP optimization. Generate and qualify leads, manage the full consultative sales cycle, negotiate contracts, prepare proposals, forecast revenue, maintain Salesforce data, and build relationships with executive and technical stakeholders. The home-based role requires 30–40% travel and regular visits to headquarters.

Own post-sale client relationships for small-business accounts, serving as a strategic advisor and driving adoption, retention, renewals, upsells, cross-sells, and ARR growth. Lead business reviews, product enablement, issue resolution, customer advocacy, and success reporting while coordinating cross-functionally with Sales, Product, Engineering, and Support. The role requires expertise in benefits administration products and regulations, strong relationship-building skills, and proficiency with CRM platforms.

Lead the vision, strategy, and organizational growth of Payments SRE across reliability, observability, automation, incident management, performance, capacity planning, and disaster recovery. Manage SRE leaders and engineers supporting high-availability, low-latency payment systems. Partner with executives and engineering and product teams, drive compliance and operational excellence, mentor leaders, build reliability frameworks, and advance AI-enabled operations.

Own enterprise IAM strategy, governance, engineering, and operations for identity lifecycle management. Lead IAM Operations and Engineering teams, modernize SailPoint IdentityIQ to Identity Security Cloud, automate joiner-mover-leaver workflows, and integrate hundreds of applications. Partner with application owners and Information Security on data standardization, testing, provisioning, and deployment. Drive RBAC/ABAC, entitlement rationalization, segregation of duties, directory modernization, ITIL adherence, automation, and measurable risk reduction.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.