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

Build and operate ML platform infrastructure supporting researchers and product engineers. Responsibilities include designing distributed systems, data pipelines, observability tools, scheduling and orchestration infrastructure, and GPU fleet systems. The role partners closely with ML research, owns reliability and performance, improves developer experience, and ships platform primitives in a high-ownership environment.

Design and iterate task sets, rewards, and environments for coding agents. Analyze agent traces and evaluations to identify failure modes, build systems to surface recurring behaviors, and convert one-off solutions into reusable training infrastructure and high-quality datasets. Partner with research teams to validate that datasets teach intended capabilities. The role requires strong software engineering fundamentals and comfort with infrastructure, data, or distributed systems.

Build large-scale data systems for frontier coding-model pretraining across data quality, data platform, or web crawling. Responsibilities include developing high-throughput pipelines, training and deploying data-quality models, orchestrating reproducible datasets, improving crawl coverage and parsing, and hardening distributed infrastructure. The role requires independent end-to-end ownership, performance optimization, observability, experimentation, and close collaboration with research, training, acquisition, and engineering teams.

Build hands-on technical learning content (labs, tutorials, workshops, demos, reference implementations) using Cursor to enable developer adoption; lead workshops; partner with Product, Engineering, DevRel, and Field teams; measure adoption and improve educational formats.

Own strategic customer engagements from discovery through delivery: scope time-boxed AI/LLM projects with executives, lead technical discovery, configure workflows and integrations, enable adoption through training and playbooks, monitor feasibility, surface expansion opportunities, and escalate product gaps to Product and Engineering.

Design and own Cursor's storage layer: databases, caches, and multi-database topology. Build sharding, migrations, query instrumentation, cache infrastructure, and guidance for choosing storage engines to ensure reliability, performance, and scalable operations.

Build platform primitives for service provisioning, deploy tooling, workflow orchestration, and service ownership at Cursor. Improve developer experience and production reliability by creating shared abstractions, declarative service configuration, durable workflows, and a service catalogue to speed provisioning and incident resolution.

Design and build security features across product, cloud, and infrastructure. Anticipate vulnerabilities, implement secure defaults, enable developer-friendly security, and ship robust protections for code, agents, and tooling.

Design and build core product software (the editor) for professional programmers. Invent new interfaces and UX for AI-assisted code workflows, run experiments and A/B tests at scale, rapidly prototype new product verticals (e.g., AI bug detection), and ship high-quality features within a small, collaborative engineering team.

Design, implement, and deploy end-to-end enterprise-grade features for an AI-enhanced product aimed at professional programmers. Work closely with research, design, and engineering to build scalable tooling for large organizations and ship production-quality software.