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

Own sales operations and deal desk processes, including pricing approvals, forecasting, pipeline and performance analysis, territory and quota planning, KPI governance, data quality, and variable compensation reporting. Partner with sales leadership, finance, and systems teams to structure enterprise deals, maintain pricing guardrails, explain revenue metrics, and improve quote-to-order workflows.

Build and scale Salesforce and broader go-to-market systems, including data models, forecasting workflows, integrations, data quality, marketplace revenue infrastructure, compensation support, and AI-powered workflows. Partner with Revenue Operations, sales, finance, marketing, data engineering, and leadership to deliver tested, source-controlled solutions and support end users across lead routing, assignments, campaigns, and GTM processes.

Own corporate IT operations across identity, access, macOS devices, SaaS applications, hardware, onboarding, offboarding, and employee support. Administer SSO and MDM platforms, manage SaaS licenses and vendors, automate repetitive workflows, maintain documentation and asset inventories, support office IT, and assist with security controls, access reviews, compliance audits, and evidence collection.

Build, secure, scale, and maintain cloud infrastructure supporting SaaS products, databases, storage, search, microservices, and machine-learning pipelines. Operate Kubernetes workloads, automate infrastructure with Terraform and Helm, improve reliability and observability, optimize costs, define SLOs and SLAs, participate in incident response and on-call rotations, address vulnerabilities, support customer security integrations, and maintain compliance with SOC 2, HIPAA, and GDPR requirements.

Help self-serve and corporate customers become successful, independent Roboflow users through technical onboarding, webinars, workshops, office hours, targeted enablement, and reusable educational content. Teach computer vision workflows including datasets, annotations, model training, evaluation, and deployment. Identify recurring customer friction, create scalable learning programs and documentation, monitor engagement, measure program effectiveness, and collaborate with Product, Support, Sales, and Field Engineering.

Build and manage Roboflow Labs’ frontier-lab business development pipeline. Own the full sales cycle, including outreach, discovery, evaluations, pricing, proposals, negotiation, procurement, legal coordination, and closing. Establish repeatable CRM, forecasting, pipeline-review, and account-planning systems while developing durable customer relationships. Represent Roboflow Labs at industry events and engage research scientists, human-data teams, technical executives, procurement, and legal stakeholders.

Conduct applied AI research focused on computer vision, multimodal, robotics, and coding models. Design datasets, evaluations, benchmarks, and reinforcement learning environments; analyze model failures; develop data remediation specifications; validate data quality through fine-tuning and ablation studies; contribute to open models; publish research; and collaborate with frontier labs to translate model roadmaps into data programs.

Build and own Roboflow Labs’ data platform, including catalog, delivery, contributor payout, and shared data infrastructure. Develop reliable pipelines for dataset ingestion, validation, packaging, provenance, rights tracking, and auditability. Automate quality controls such as consensus review, fraud detection, duplicate detection, and quality metrics. Establish engineering standards for architecture, observability, code review, releases, and on-call practices while partnering with operators and integrating with Roboflow’s core platform.

Own Roboflow Labs’ contributor supply-side growth, managing the funnel from awareness and application through onboarding, activation, retention, and reactivation. Build acquisition channels, referral programs, partnerships, and expert contributor cohorts. Set compensation benchmarks, optimize incentives, and protect network quality through screening, fraud prevention, identity verification, and contributor experience improvements.

Own end-to-end delivery of datasets and environments for leading AI labs, including intake, scoping, execution, quality review, and shipment. Build measurable quality processes, collaborate with researchers and engineers to shape data products, and manage capacity across contributor and reviewer networks. The role requires strong organization, judgment, communication, and practical experience with agents, evaluations, or reinforcement learning workflows.