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

Design, deploy, and operate cloud networking architectures (VPC/VNet, transit hubs, hybrid connectivity, edge networking, DNS, private connectivity). Automate with Terraform, secure networks with firewalls/NACLs/DDoS protections, manage Kubernetes networking, build observability, troubleshoot packet-level issues, and collaborate across product, security, and engineering teams to deliver scalable, cost-optimized cloud network solutions.

Lead enterprise architecture and design highly scalable, fault-tolerant distributed systems. Define standards for microservices, API governance, data architecture, observability, CI/CD, and reliability engineering. Drive modernization, performance tuning, incident response, and mentor senior engineers while partnering with leadership on technology roadmap and platform consolidation.

Architect, deploy, and operate large-scale Apache Kafka/Confluent clusters; implement security, HA/DR, Connect, Schema Registry, and stream processing; build GitOps CI/CD and IaC; implement observability; onboard teams; lead incidents and mentoring; maintain documentation.

Design, develop, and support PeopleSoft customizations, integrations, and reports across HCM/FSCM/Campus Solutions. Build Application Engine programs, Component Interfaces, Integration Broker services, SQR/BI Publisher/nVision reports, optimize SQL, lead testing and hypercare, document solutions, and mentor junior developers while minimizing core modifications for upgradeability.

Design and implement high-performance backend services and cloud-native applications in Go. Build Kubernetes controllers/operators, APIs (gRPC/REST), CLI tools, and integrations with messaging systems and data stores. Profile and optimize performance, implement observability, write comprehensive tests, lead reviews, mentor engineers, and maintain technical documentation.

Lead design and evolution of large-scale microservices ecosystems: define service boundaries, decomposition, communication patterns, data ownership, resilience, observability, CI/CD, and platform capabilities. Modernize monoliths, establish API/schema governance, mentor engineers, drive performance and scalability, and evaluate cloud-native technologies to ensure secure, maintainable, high-traffic production systems.

Optimize end-to-end training and inference for large neural networks by profiling pipelines, eliminating bottlenecks, implementing quantization/pruning, tuning distributed training (tensor/pipeline parallelism, FSDP/ZeRO), driving compiler/kernel optimizations (Triton/XLA/TVM), building benchmarks, and advising on hardware/software and cost-efficiency to translate research advances into production gains.

Design, prototype, and evaluate applied AI solutions across language, vision, recommendation, and structured data. Translate business problems into ML formulations, run rigorous experiments, build production training and inference pipelines, optimize models for accuracy and latency, develop dataset tooling and monitoring, ensure safety and fairness, document findings, mentor engineers, and influence the AI roadmap.

Design and own multi-cloud strategy and reference architectures (AWS/Azure/GCP/OCI). Build federated identity, cross-cloud networking, IaC patterns, Kubernetes/container and serverless strategies, cross-cloud data and observability approaches, FinOps, DR/BC, and security/KMS models. Produce architecture artifacts, guide engineering trade-offs, and engage providers and stakeholders to operationalize multi-cloud platform capabilities.

Design, execute, and operationalize fine-tuning workflows for LLMs (supervised, DPO, RLHF). Lead dataset curation, build scalable distributed training pipelines, optimize stability and throughput, implement parameter-efficient tuning, design rigorous evaluation and safety tests, operate GPU cluster training jobs, manage model artifacts and reproducibility, and mentor engineers while translating research into production-ready recipes.