Jobs at Surge AI

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.

Manage end-to-end product operations, coordinating product, engineering, and operations teams to ensure efficient feature and solution delivery. Track and optimize performance metrics, resolve operational roadblocks, manage product-update communications, and use data-driven insights to improve processes and support product success. The role requires strong analytical, communication, organizational, and continuous-improvement skills in a fast-paced, ambiguous environment.

Leads cross-functional technical programs across engineering, operations, product, customer support, and business development. Establishes scalable systems and processes, coordinates feature implementation, manages multiple concurrent projects, sets timelines, and drives teams toward deadlines. The role requires strong stakeholder communication, influence without authority, comfort with ambiguity, and the ability to balance competing priorities in a rapidly scaling AI-focused company.

Leads cross-functional initiatives spanning customer support, operations, product, engineering, and business development. Establishes scalable systems and processes, coordinates feature development, manages multiple projects, sets timelines, and drives teams toward deadlines. The role requires strong stakeholder communication, influence without authority, comfort with ambiguity, prioritization skills, and an interest in AI systems.

Conduct full-cycle recruiting for technical and non-technical roles, including sourcing, screening, scheduling, candidate closing, and offer coordination. Own searches from intake through close, source candidates creatively, partner directly with hiring managers, assess market fit, manage multiple searches with urgency, and improve recruiting processes. Technical recruiting experience is a plus, while strong writing, autonomy, and judgment are essential.

Own the full lifecycle marketing program for a contributor workforce, including onboarding, activation, engagement, retention, and reactivation. Lead email strategy, segmentation, deliverability, testing, copywriting, and performance analysis. Run paid marketing experiments, productize marketing playbooks, and apply UX sensibility to communications. Collaborate with product, design, operations, and growth teams on positioning, messaging, launches, and platform improvements.

Design scalable human-data collection systems for AI model post-training, including task frameworks, data workflows, quality assurance, evaluation datasets, and delivery pipelines. Partner with engineering and product teams to improve throughput and reliability while preserving data quality. Translate collection and quality patterns into insights that improve project design, client deliverables, and product development.

Oversee end-to-end product operations, coordinate with product and engineering teams, track performance metrics, resolve operational roadblocks, manage product communications, and use data-driven insights to improve delivery and operational efficiency.

Conduct hands-on research with frontier AI labs to evaluate model behavior, identify failure modes, and design interventions that improve reliability. Responsibilities include designing evaluations, building data pipelines and fine-tuning workflows, collaborating with internal and external stakeholders, and addressing challenges involving long-context reasoning, tool use, and real-time feedback loops.

Research data strategies for large language model development, including designing data slices to identify model failure modes, modeling annotator behavior, and optimizing instruction clarity and reward-signal reliability. Develop metrics and evaluation frameworks to assess dataset quality, diversity, alignment impact, and downstream model performance. Collaborate with AI research teams and test data-centric methodologies in production environments.

Design and govern reinforcement learning environments, including modular APIs, curricula, reward and termination schemas, quality standards, telemetry, and evaluation suites. Build robust simulations of real-world tasks, synthetic data generators, and multi-agent ecosystems while mitigating reward hacking, mode collapse, and exploitable loopholes. Partner with research and engineering teams to create deterministic, observable, scalable environments and maintain reproducible, high-quality training signals.

Design and build core fullstack systems powering Surge’s AI platform. Own frontend product decisions, backend architecture, and high-impact workflows for data collection, labeling, and evaluation. Ship scalable, reliable, intuitive tools while making autonomous technical decisions, collaborating with engineering and machine learning leaders, and maintaining high standards for usability, performance, and maintainability.