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

Design, build, and scale TypeScript/Node.js backend services, APIs, microservices, databases, integrations, and production infrastructure for a conversational AI healthcare platform. Own services across deployment, monitoring, security, compliance, incident response, and reliability. Implement authentication and authorization, CI/CD, observability, and scalable real-time systems while mentoring engineers and collaborating cross-functionally.

Own end-to-end data science and AI evaluation projects, developing frameworks, metrics, golden datasets, benchmarks, pipelines, and dashboards for LLM- and speech-based systems. Analyze failure modes, calibrate LLM judges against human ground truth, and drive continuous product improvement. Collaborate with AI/ML engineers and product managers, contribute to technical strategy, mentor junior data scientists, and establish evaluation best practices.

Own customer-facing analytics and reporting for a voice AI healthcare product. Lead customer data implementations, troubleshoot ingestion and mapping issues, prepare internal teams for campaign execution, develop dashboards, define performance metrics, and standardize reporting. Coordinate data and QA engineers through agile delivery while scaling onboarding workflows, improving implementation velocity, and expanding analytics capabilities for payer and provider customers.

Build and operate highly reliable, scalable production, development, and QA environments for AI/ML services, APIs, and web applications. Responsibilities include Kubernetes cluster management, CI/CD and GitOps automation, security scanning integration, observability, access controls, audit logging, disaster recovery, availability improvements, and cloud cost optimization. The role requires strong cloud, infrastructure-as-code, DevOps, programming, and security experience.

Lead the post-sales lifecycle for enterprise healthcare customers deploying Ellipsis Health’s conversational AI care manager. Own implementation delivery, adoption, retention, expansion, lifecycle operations, sales-to-delivery alignment, product feedback, and customer success playbook development. Partner cross-functionally with Commercial, Product, Engineering, and Operations to deliver measurable clinical and operational value, establish predictable deployment processes, and scale customer success operations.

Lead design and deployment of production-grade agentic AI systems for healthcare. Architect scalable multi-agent orchestration, data curation pipelines, evaluation frameworks, and translate ML research to secure, low-latency clinical applications while mentoring engineers and collaborating with clinical and product teams.

Lead QA for end-to-end system and user workflows by performing manual and automated testing, building and maintaining automation frameworks, integrating tests into CI/CD, validating integrations and distributed systems, tracking defects, supporting release and regression validation, and improving testing strategy and quality metrics in Agile teams.

Design and maintain backend infrastructure and tooling for an AI evaluations platform. Build production-grade evaluation frameworks for ASR, LLMs, TTS, knowledge bases, and guardrails. Implement API/data integrations, test configuration management, reporting, observability, and debugging tools. Partner with ML, engineering, and QA to operationalize evaluations, enable regression detection and continuous monitoring, and improve developer experience and reliability.

Drive nationwide provider sales to large health systems: build pipeline, generate leads, lead RFPs, develop executive relationships, close complex deals, and collaborate cross-functionally to grow revenue and shape commercial strategy.

Lead design, build, and operate a secure, scalable data platform to support analytics, MLOps, and BI. Architect data warehousing (Databricks, dbt, Spark), build ETL pipelines, optimize cloud storage and processing in GCP/AWS, implement LLM infrastructure and serving, create observability and BI dashboards, and ensure data governance and regulatory compliance (HIPAA/SOC-2). Collaborate with Data Science, ML, App, and DevOps teams.