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

Lead Elasticsearch performance engineering from architecture through production. Develop performance models, optimize distributed systems, profile bottlenecks, build benchmarks, detect regressions, and improve scalability across stateful and serverless architectures. Design AI-assisted optimization and benchmarking tools, collaborate across teams, define performance strategies, and mentor engineers. The role requires deep Java, JVM, concurrency, distributed systems, and search or data-store performance expertise.

Develop and optimize Elasticsearch performance across distributed and serverless architectures. Responsibilities include implementing performance models, profiling systems, identifying bottlenecks, building benchmarks and regression detection, improving logging, metrics, vector search, and ES|QL performance, and creating AI-assisted optimization tools. The role also involves architectural and code-level optimization, technical analysis, peer reviews, and guidance for fellow engineers.

Drive net-new Mid Market SaaS revenue by building outbound pipeline, qualifying opportunities with MEDDPICC, delivering executive demos/value stories, negotiating multi-year consumption deals, and partnering with Solutions Architects, Customer Success, Marketing, and RevOps to close complex, multi-stakeholder opportunities.

Design and build autonomous, enterprise-grounded agentic workflows using RAG and ESRE. Integrate and fine-tune LLMs with internal APIs and SaaS tools. Provision scalable cloud infrastructure via Terraform, implement CI/CD, secure network architectures, and deploy observability and tracing to monitor agent behavior. Provide technical leadership, documentation, and enable internal teams to leverage the Elastic Stack for automated business task completion.

Lead AMER sales enablement by diagnosing performance gaps using Gong/Salesforce/Clari data, designing targeted remediation, delivering blended training (MEDDPICC, value selling, pipeline forecasting, GenAI usage), embedding methodology through deal clinics, driving tool adoption, measuring enablement impact with RevOps, and partnering cross-functionally to connect training to sales outcomes.

Design, implement, and optimize Elasticsearch core storage and query engine components for observability workloads. Drive architectural improvements, resolve performance bottlenecks in indexing and serialization, optimize data layouts for object storage, lead cross-organizational initiatives, mentor engineers, triage community issues, and write idiomatic modern Java in a distributed team.

Develop and maintain core Elasticsearch infrastructure and shared platform features. Design scalable APIs, solve performance and concurrency issues, improve observability and CI/CD, automate developer workflows, lead cross-team projects, support complex operational investigations, and use AI to accelerate development and debugging.

Drive architecture and technical vision for Elasticsearch core storage and query engine. Lead performance and storage-efficiency improvements, design indexing/serialization/data layouts, mentor engineers, coordinate cross-organizational initiatives, triage community issues, and write idiomatic modern Java for logs and metrics workloads.

Design, build, and improve Elasticsearch distributed-systems components for scalable, concurrent, and consistent indexing. Maintain cluster coordination for performance and resilience, push system scale limits, investigate performance and concurrency issues, support operations/support engineers, and lead projects involving multiple engineers from design through delivery.

The role involves improving Elasticsearch's distributed components, maintaining cluster coordination systems, and addressing complex issues to optimize performance and scalability.