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

Generate qualified sales opportunities through outbound prospecting across focus accounts. Research prospects, personalize outreach using AI tools, engage buyers through calls, email, LinkedIn, and events, qualify business needs, and book meetings for Account Executives. Maintain accurate Salesforce records, share prospect feedback, test outreach approaches, and improve team workflows. Success is measured by qualified meetings, pipeline contribution, conversion, and consistent execution.

Own full-cycle recruiting for go-to-market roles across sales, marketing, customer success, and revenue operations. Develop data-driven sourcing strategies, advise hiring managers, manage recruiting pipelines in Ashby, and deliver strong candidate experiences. Contribute to employer branding, process improvement, DEI, training, and talent planning while maintaining a SaaS sales and marketing network and mentoring junior recruiters.

Own the end-to-end data science lifecycle for workforce identity and hiring fraud detection. Analyze identity, device, behavioral, resume, and application data; develop fraud rules, machine learning models, and anomaly detection systems; and support deployment and monitoring in RiskOS. Collaborate on GenAI resume verification and explanation tools, evaluate LLM components, partner with engineering on productionization, and communicate model performance and customer impact to product, GTM, and consulting stakeholders.

Executive leader responsible for channel and partner ecosystem strategy, joint revenue growth, market expansion, and strategic alliances. Builds reseller, technology partner, and systems integrator relationships; aligns Sales, Product, Marketing, and Customer Success; establishes channel program standards; uses performance data to improve outcomes; and leads, mentors, and develops a high-performing channel leadership team through organizational change.

Develop and deploy deep learning models for fraud detection, risk management, and identity verification. Work across diverse data modalities, including tabular data, text, point clouds, and images, throughout the machine learning lifecycle. Responsibilities include data exploration, feature engineering, model training, evaluation, deployment, monitoring, and communicating results. Collaborate cross-functionally, make technical decisions independently, and apply emerging AI and machine learning techniques to production challenges.

Own centralized Product Operations systems, reporting, analytics, and process governance. Build dashboards and metrics, improve data quality, standardize planning and prioritization workflows, facilitate cross-functional handoffs, and connect product strategy to roadmap execution. Support planning cycles, business reviews, and leadership reporting while enabling embedded product teams through documentation, training, tooling, and repeatable processes.

Leads requirements gathering, planning, implementation, deployment, and concurrent project delivery for global clients. Manages timelines, scope, dependencies, stakeholder communications, and status meetings while partnering with commercial, technical, development, and deployment teams. Diagnoses integration challenges, educates clients on solutions, supports sustainable implementations, and provides product roadmap insights. Requires strong technical project management, agile development knowledge, communication, analytical problem-solving, and relationship-building skills.

Provides technical leadership for document verification machine learning solutions. Oversees the full ML lifecycle, including data strategy, model development, evaluation, deployment, monitoring, and improvement. Mentors data scientists, establishes technical standards, identifies risks, and improves model performance, scalability, and reliability. Partners with engineering, product, and customers to translate needs into modeling strategies and communicates technical decisions to diverse stakeholders.

Own the zero-to-one development and commercialization of Socure’s proactive identity notification products. Define the roadmap, event contracts, data-source strategy, KPIs, architecture, pricing, packaging, beta, and general availability launch. Evaluate data sources, measure coverage and precision, guide platform engineering decisions, recruit design partners, and coordinate cross-functional delivery. The role combines technical product management, data analysis, customer research, vendor and licensing judgment, and go-to-market ownership.

Lead end-to-end product design for complex enterprise, developer-facing, and AI-assisted identity and fraud-prevention products. Conduct research, frame problems, map workflows, prototype and test solutions, deliver detailed designs, and partner with engineering through implementation. Design robust states, permissions, errors, and operational workflows while contributing to the design system. Use AI tools to accelerate research, prototyping, documentation, and QA while protecting user trust and applying independent judgment.