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

Build and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Own complex architectures, reliability SLOs, security and compliance reviews, technical roadmaps, and cross-functional delivery while mentoring engineering teams.

Designs, builds, deploys, and maintains production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production models, and applying responsible AI, governance, and security practices. The role collaborates with Product, Data Science, and Agile engineering teams to deliver scalable ML solutions.

Develop and deploy production machine learning models and infrastructure at scale. Responsibilities include model development, data pipeline construction, cloud and Kubernetes operations, distributed data processing, CI/CD automation, model monitoring and retraining, responsible AI governance, and collaboration with product and data science teams. The role requires strong programming, machine learning, distributed systems, and production deployment expertise.

Leads project and program management across feasibility, planning, prioritization, implementation, and closeout. Establishes structure, methodologies, documentation, metrics, and transparency in complex environments. Partners with cross-functional stakeholders, influences delivery, manages competing priorities, and develops high-performing teams. Requires extensive project or process management and people leadership experience, with responsibility for strategic and tactical execution.

Build and lead full-stack software engineering initiatives using distributed microservices, cloud platforms, APIs, databases, and modern front-end technologies. Provide technical leadership across Agile teams, establish engineering standards, mentor developers, partner with architects and product managers, reduce technical debt, improve system performance, and influence long-term technology strategy. The role emphasizes AWS and other cloud technologies, open-source frameworks, CI/CD, automated testing, observability, and AI-assisted development tools.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.

Leads technical vision and architecture for customer resiliency systems. Designs scalable, operable solutions; modernizes monoliths into microservices and event-driven platforms; establishes API and engineering standards; and contributes hands-on across the software lifecycle. Collaborates with senior leaders, mentors engineers, drives cross-divisional initiatives, promotes engineering excellence, and advises on enterprise architecture, cloud computing, data architecture, performance, and scalability.

Lead cloud infrastructure strategy and innovation, including cloud connectivity, landing zones, platform management, modernization, networking, security, identity, and automation. Define technical vision and API standards, guide enterprise architecture, improve large-scale distributed systems, and promote AI-enabled engineering workflows. Mentor engineers, lead cross-domain initiatives, influence technology strategy, and ensure reliable, secure, scalable cloud operations.

Design, develop, optimize, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, orchestration, governance, and observability. Lead cost-performance optimization across production AI platforms, establish engineering standards, contribute to technical roadmaps, lead design reviews, and mentor senior AI engineers. Collaborate with research, engineering, program management, and product teams to deliver scalable, responsible AI products.

Lead technical vision and architecture across engineering domains, decompose complex problems, and ensure scalable, operable implementations. Modernize monolithic systems into microservices and event-driven platforms, define API and platform standards, and guide cross-divisional initiatives. Provide hands-on technical contributions while mentoring engineers, promoting engineering excellence, recruiting talent, and communicating technical knowledge across the organization.