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

Own the product vision and roadmap for AI-driven growth products. Engage customers to gather feedback, define requirements, and prioritize features with engineering. Serve as a trusted advisor to drive adoption and measurable customer growth. Collaborate across data, engineering, and business teams to execute in a fast-paced, distributed environment.

Early-career seller who will learn Hilbert’s product in San Francisco (~3 months) then deploy internationally (6–12 months) to build and own a market: generate enterprise pipeline from scratch, close first strategic accounts, form local partnerships, map buyers/competition, and feed market insights back to HQ to establish a permanent regional presence.

Lead and own the data science function: design and build production ML systems (recommendation, forecasting, segmentation), develop configurable multi-tenant models, run rigorous experimentation and causal analyses, connect model outputs to business impact, set scientific standards, and hire and mentor a high-velocity data science team.

Own end-to-end data science and ML work for B2C customers: build recommendation, forecasting, segmentation, and activation models; design A/B tests and causal analyses; create configurable multi-tenant model architectures; extract signal from sparse/noisy data; partner with engineering to productionize models and deliver analyses that change business decisions.

Architect and productize Hilbert's portable infrastructure: build and maintain AWS/K8s infrastructure, create an enterprise on‑prem package, convert scripts into reusable Helm/Terraform/Docker artifacts, automate internal tooling (queues, Redis, Mongo), and lead SOC2/security and zero‑trust strategy.

Build and optimize core data platform: improve ClickHouse performance, create configurable ingestion schemas, develop shared Python libraries, design integration pipelines, and architect monitoring/observability for reliable, production-grade data delivery.

Build and own end-to-end product surfaces for an AI agent: frontend React dashboards, backend agentic query engines, enterprise-ready services, admin tools, and integrations. Collaborate with Product and ML teams to design, ship, and operate scalable, secure, configurable systems.

Design, build, and maintain production-grade AI systems and agent-based workflows; own end-to-end pipelines from experimentation to deployment and monitoring; build evaluation pipelines; collaborate with product, data, and GTM; iterate quickly under ambiguity to shape the AI stack.

Lead Data, Software, and Platform engineering to build a high-velocity, enterprise-grade AI growth platform. Own roadmap, architecture, security (SOC2/ISO), hiring, and delivery for mid-market integrations and custom on‑prem enterprise deployments.