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

Launch and manage the company’s outbound pipeline function from scratch. Prospect and qualify technical buyers through email, social, phone, and events; build lead lists and automated outreach workflows; schedule qualified meetings for company leadership; and continuously test messaging and channels. The role requires quota-carrying SDR experience, comfort with AI and automation tools, credible communication with technical audiences, and willingness to work onsite five days per week.

Own the architecture and delivery of backend systems, data pipelines, algorithms, and scalable cloud infrastructure for a real-time conversational software platform. Build systems that analyze software performance, surface insights, and flag failures. Drive projects from design through release, make technical decisions, and work directly with customers to deploy and demonstrate the product.

Own the quality measurement framework for AI models and software features. Build evaluation suites, human review rubrics, dashboards, reporting tools, and instrumentation that measure capability, behavior, regressions, and real-world usage. Establish release quality standards, audit existing evaluations, close measurement gaps, and collaborate with research, product, business, and external partners. The role requires protecting quality standards under product deadlines and shaping future evaluation and research priorities.

Build backend systems integrating machine learning with traditional infrastructure, deliver features end-to-end, and collaborate closely with founders and engineers. Help establish engineering practices, culture, and processes as an early hire while iterating quickly in a small, evolving team.

Own and mature the application security program, including secure development practices, tooling, automation, vulnerability management, and detection pipelines. Partner with engineering on secure AI-driven workflows, data privacy, and AI-specific vulnerabilities. Lead incident response as primary incident commander, support cloud security, and guide security audits, certifications, and customer requirements in a regulated fintech environment.

Own the end-to-end design, development, and operation of backend and platform systems. Build durable workflow engines, identity and authorization systems, policy engines, secure credential management, isolated virtual environments, and production interfaces and APIs. The role requires strong systems architecture, security expertise, cross-functional engineering ability, and comfort operating in ambiguity at an on-site startup.

Lead technical direction for a major software platform domain, guiding system design, long-term strategy, and complex engineering decisions. Build and scale high-traffic backend systems or AI-driven products, establish engineering standards, collaborate with product and engineering executives, and improve effectiveness across multiple teams. The role requires strong distributed architecture, database or large language model expertise, startup experience, and hands-on use of AI coding assistants.

Lead applied machine learning research from ideation through training, evaluation, and production deployment. Fine-tune language and multimodal models using reinforcement learning, develop autonomous agents and simulated environments, build data pipelines and evaluation suites, and investigate planning, memory, context retention, error recovery, and human-agent interaction. The role also contributes to research priorities, technical direction, and complete production-ready systems.

Architect and operate infrastructure for distributed large-model training and deployment across accelerator fleets. Build scheduling tools, high-volume multimodal data pipelines, model compression and compilation workflows, and observability systems. Optimize compute, storage, and memory performance while partnering closely with researchers to accelerate experimentation and production deployment. The role requires technical leadership, PyTorch expertise, distributed training experience, ML platform ownership, multimodal systems experience, and on-site work five days per week.

Lead research and development of compact machine learning models for consumer hardware. Own model capabilities from data selection through experimentation, training, compression, validation, and production release. Apply fine-tuning, reinforcement learning, and efficient inference techniques while optimizing memory and GPU performance. Collaborate with engineers and researchers, document results, prioritize promising research directions, and deliver models that improve measurable quality and outperform existing benchmarks.