Top Tech Jobs & Startup Jobs

Owns company-wide security across product, infrastructure, corporate systems, and IT. Responsibilities include fixing authentication and authorization issues, managing keys and secrets, hardening internal tools, improving permissions, contributing to backend engineering, and leading security reviews, incident response, and security tooling. This hands-on role requires broad security expertise, strong software engineering ability, and comfort working in an ambiguous early-stage startup environment.

Own end-to-end growth across contributor acquisition, reactivation, retention, and platform engagement. Lead workforce recruiting, channel experimentation, marketing and partnership initiatives, and cross-functional projects. Use data to identify opportunities, build models, run experiments, and scale effective tactics. The role requires AI/LLM familiarity, paid and organic campaign experience, strong communication, comfort with ambiguity, ownership, and senior-level execution.

Drives growth by identifying strategic partnerships with enterprise AI and machine learning companies. Develops business development strategies, builds relationships with senior decision-makers, negotiates complex deals, and collaborates with internal teams to implement partnerships. Requires strong AI/ML industry knowledge, executive communication skills, strategic thinking, and adaptability in a fast-paced startup environment.

Leads cross-functional programs that improve products, customer support, and value delivery. Owns execution, coordinates engineering and operations teams, establishes scalable processes, uses data-driven insights to improve customer models, scopes and sequences major initiatives, and communicates progress to stakeholders. The role requires program management experience, strong communication and influence skills, comfort with ambiguity, rapid execution in scaling environments, and interest in AI systems.

Manage customer relationships and AI-focused engagements, serving as a trusted advisor and primary point of contact. Oversee data projects, ensure SLA and delivery goals, coordinate with engineering and operations teams, address client concerns, and translate technical AI and machine learning requirements into actionable project plans.

Oversee product operations from planning through delivery, coordinating product, engineering, and operations teams. Track and optimize performance metrics, improve operational processes, manage product communications, resolve delivery roadblocks, and use data-driven insights to support scalable product execution and continuous improvement. Interest in AI/ML systems is beneficial but not required.

Build scalable data pipelines, automation workflows, internal tools, and AI-powered quality-control systems. Lead data collection projects for frontier AI labs, establish quality frameworks, improve customer support processes, and manage cross-functional execution. Collaborate with technical and non-technical stakeholders to solve ambiguous operational challenges and deliver datasets and systems that improve advanced AI models.

Own the direction and execution of critical AI development products. Build AI-assisted annotation tools, data exploration and visualization interfaces, RLHF workflow products, large-scale data processing systems, and tools for communicating complex instructions. Make autonomous technical and product decisions, lead ambiguous projects, ship rapidly, and collaborate with engineering and research leaders to support AI training and evaluation.

Leads cross-functional AI programs spanning customer support, product, operations, engineering, and business development. Responsibilities include building scalable systems and processes, coordinating feature implementation, managing multiple projects, setting timelines, driving execution, and aligning technical and non-technical stakeholders. The role suits an early-career professional comfortable with ambiguity, competing priorities, influence without authority, and rapid organizational growth.

Manage end-to-end data operations, including collection, processing, delivery, pipeline optimization, and data quality monitoring. Collaborate with engineering, product, and operations teams to improve data workflows, identify inefficiencies, and implement scalable solutions. Analyze data to provide actionable insights for decision-making and AI model development while communicating technical issues to non-technical stakeholders.