Jobs at Prolific

Build full-stack software solutions for frontier AI companies and enterprise customers. Translate customer and business problems into scalable products, collaborate with account managers and customers, work across shared codebases and technology domains, and support production systems and incident response. The role requires strong product judgment, rapid iteration, direct customer communication, and the ability to balance startup speed with reliable engineering.

Manage the operational delivery of human data for a strategic AI customer. Responsibilities include translating requirements into workflows, managing participant pools and onboarding, tracking supply and quality, leading cross-functional projects, resolving operational issues, documenting processes, and improving scalability with Product and Engineering. The role also supports participant assessment and certification programs while serving as an operational point of contact for delivery teams.

Own operational delivery for complex research customer projects, including participant acquisition, onboarding, verification, activation, quality monitoring, and supply development. Build project plans, workflows, playbooks, and scalable processes while optimizing participant funnels and operational metrics. Partner with product, data, science, and customer success teams to improve time-to-data, fill rates, participant quality, and customer outcomes across Prolific’s Research platform.

Design, build, and maintain scalable data pipelines and platform services to support analytics, ML, and product teams. Implement infrastructure-as-code (Terraform) and Kubernetes-based deployments, develop data services/APIs, and ensure data quality, observability, security, and compliance. Partner with cross-functional teams, document systems, and continuously improve performance and reliability.

Leads complex AI client projects from requirements gathering through delivery, serving as the primary client contact and coordinating Sales, Operations, Product, and Engineering. Responsibilities include defining scopes and delivery plans, managing risks, quality, timelines, budgets, profitability, and KPIs, resolving execution challenges, and improving scalable processes for AI project delivery.

Owns end-to-end human data operations for specialized participant pools supporting AI projects. Responsibilities include participant onboarding, verification, quality management, engagement metrics, process optimization, project planning, documentation, and stakeholder enablement. Partners with product, technology, data science, delivery, and customer success teams to scale manual workflows into technology-driven solutions and align participant capabilities with client-specific AI requirements.

Partner with AI labs and enterprise teams on evaluation and human-data projects. Design methodologies, rubrics, data-collection approaches, quality frameworks, verifiers, validation systems, and reinforcement learning environments. Own projects from scoping through delivery, prototype tooling, advise customers, identify platform opportunities, collaborate with Product and Engineering, contribute to research publications, mentor colleagues, and communicate advanced AI concepts to diverse stakeholders.

Manage end-to-end operations for specialized participant pools supporting AI projects. Responsibilities include participant onboarding, verification, quality management, engagement monitoring, process optimization, project planning, documentation, and stakeholder enablement. Partner with product, technology, data, science, delivery, and customer success teams to scale manual workflows into technology-driven solutions and support high-quality human data collection for AI tasks.

Analyzes human-generated data quality across AI programs using SQL, Python, statistical methods, and hands-on annotation review. Investigates quality issues, identifies failure patterns, measures impact, and develops recommendations. Supports quality pipelines, automated validations, rubrics, gold sets, calibration exercises, reporting, and dashboards. Collaborates with quality engineering, operations, delivery, and engineering teams to improve annotation design, training, tooling, and quality controls.

Provides senior technical leadership for an engineering team by shaping technical direction, architecture, roadmaps, and engineering standards. Leads technical discovery and solution design, drives code quality, testing, reliability, and performance, and collaborates across teams. Mentors engineers, influences technical decisions, and connects engineering work to customer and business outcomes while contributing hands-on.

Lead Cloud Platform and SRE teams, guiding infrastructure strategy, Kubernetes operations, platform reliability, developer tooling, and technical growth. Own SLOs, SLAs, error budgets, observability, incident remediation, and secure cloud environments. Drive self-service infrastructure, GitOps, infrastructure-as-code, and platform adoption while balancing stability with developer velocity. Partner cross-functionally to communicate technical decisions and build scalable, resilient systems.

Own end-to-end delivery of complex AI client projects, translating evolving requirements into project scopes, delivery plans, and successful human data programs. Manage client relationships, cross-functional coordination, risks, quality, timelines, budgets, profitability, and delivery metrics. Partner with Sales, Operations, Product, and Engineering while improving scalable processes for AI project execution.

Build practical machine learning, statistical, and data science systems for Prolific’s participant marketplace. The role focuses on modeling participant supply, demand, and quality; improving matching to studies; validating useful signals; designing experiments and metrics; and deploying production-ready solutions. The scientist will independently shape ambiguous problems, select appropriate approaches ranging from classical statistics to LLMs, and collaborate with product, engineering, and Supply Data Science specialists.

Conduct independent AI research focused on evaluation methodologies, alignment techniques, and synthetic data generation. Design rigorous experiments and scalable frameworks for evaluating LLMs and agent systems using human data. Build research infrastructure and practical tools, applying fine-tuning, optimization, quantization, adapters, and distillation. Publish research, contribute to open source, collaborate with engineering and product teams, mentor colleagues, and communicate complex AI concepts clearly.

Own net-new and expansion enterprise sales cycles with frontier AI labs across the West. Build relationships with AI research, product, and executive stakeholders; manage complex, multi-threaded technical sales processes; develop proposals, pilots, and success plans with cross-functional teams; forecast pipeline and communicate deal health; and relay customer feedback to product and marketing. The role requires extensive experience selling strategic B2B technology solutions, including seven- and eight-figure deals.

Own analytics across the participant lifecycle to support acquisition, onboarding, activation, engagement, quality, retention and reactivation. Build metrics, dashboards, and experimentation frameworks with SQL and Python; partner with Product, Operations, Growth, and Data Science to run experiments and translate marketplace data into actionable decisions that improve participant health and supply sustainability.

Manage and optimise GKE Kubernetes clusters using Terraform; implement GitOps (ArgoCD) and developer golden paths; build infrastructure for safe, scalable deployments; support cloud compliance (SOC2, ISO-27001, Cyber Essentials); produce developer-facing documentation; act as GCP/Kubernetes technical lead and participate in on-call rotation.

Lead and grow a product marketing function for Prolific's human data platform. Own positioning, messaging, launches, packaging, and go-to-market strategy; manage three PMMs; align Product, Sales, and vertical marketing; run market and competitive research and build sales enablement.

Own the end-to-end PLG strategy for Prolific's self-serve funnel: drive acquisition, activation, engagement and retention. Develop and optimise onboarding, lifecycle and in-product messaging, referral programmes and paid acquisition across search, social and display. Improve organic growth via SEO and content, define PQLs, partner with Product, Sales, RevOps and CS, and lead data-driven experimentation to measure and optimise funnel performance.

Own end-to-end product-led growth (PLG) for Prolific's self-serve B2B SaaS customers: drive acquisition, activation, engagement and retention across paid and organic channels, build onboarding and lifecycle programs, define PQLs, optimize paid strategy, partner with Product/Sales/RevOps/CS, and run experimentation to improve funnel metrics.