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

Build reinforcement learning systems from the ground up, including environments, reward functions, training pipelines, evaluation frameworks, and production systems. Train and fine-tune models using RL methods, scale training across GPU clusters, conduct experiments, and guide technical direction. The role also contributes to engineering culture and future hiring while working closely with company founders in an on-site early-stage environment.

Build and extend a Kubernetes platform using Go, custom operators, GitOps, Terraform, Helm, distributed storage, observability, container networking, and federated multi-cluster architectures. Develop automation and infrastructure tooling, support cloud-native workloads, contribute to open source, and establish platform architecture patterns as an early team member.

Own end-to-end company acquisition for a talent marketplace, from prospecting and outbound outreach through client onboarding, activation, and success. Build relationships with startup founders and talent leaders, understand hiring needs, generate demand through campaigns, partnerships, events, and referrals, and improve funnel conversion. Experiment with messaging, channels, segments, and commercial models, then turn successful approaches into scalable processes. Partner with talent and product teams to improve matching quality, client experience, and marketplace growth.

Leads development of agentic AI capabilities that execute multi-step workflows across desktop engineering tools. Owns agent architecture, evaluation frameworks, benchmarking, cost efficiency, user-story validation, and production delivery. Serves as player-coach for a small AI engineering team, writing code, reviewing designs, and setting technical direction. Requires expertise in LLM application architecture, real-world agents, Python, desktop automation, engineering software domains, and enterprise deployment constraints.

Build and own the recruiting marketplace’s AI/ML intelligence layer, including recommendation, ranking, retrieval, and LLM-powered candidate-role matching. Ship production features rapidly, architect data pipelines and model services, and translate founder product goals into deployed systems. The role requires applied machine learning, recommender systems, production LLM API experience, and strong Python or TypeScript skills. Work is fully on-site in San Francisco six days per week.

Build and own full-stack AI systems for hospitality, including response generation, retrieval, tool orchestration, evaluation, observability, no-code controls, voice capabilities, infrastructure, data pipelines, and integrations. The role focuses on making AI agents reliable in real-world conversations with messy data and failing third-party APIs while optimizing performance and cost.

Own and optimize growth programs across cold email, paid acquisition, content, SEO, partnerships, and events. Develop scientific testing processes, manage email deliverability, launch campaigns across Meta, LinkedIn, and Google, create high-velocity branded content, execute SEO strategy, and drive partnerships and event attendance through measurable outcomes.

Owns the architecture, infrastructure, evaluation, and quality improvement of multi-channel AI marketing agents. The role develops measurable agent evaluation systems, feedback loops, SEO and GEO optimization, model strategies, and supporting data pipelines. It also involves hiring and leading the AI team as the startup grows, while partnering directly with the founder.

Build and own an applied AI intelligence layer for construction documents. Set technical direction across computer vision, LLMs, and AI systems; select and coordinate detection, segmentation, OCR, and reasoning models; rapidly ship 0-to-1 prototypes; and harden them using user feedback. The role requires handling ambiguous problems, messy data, edge cases, and end-to-end production deployment as a founding engineer.

Build and ship production infrastructure for reliable AI agents handling complex, multi-step tasks. Responsibilities include agent execution, retrieval and memory systems, evaluation harnesses, production improvements, customer collaboration, orchestration, and hiring. The role requires owning the full development cycle and improving agent performance using real-world feedback.