Jobs at Appen

Leads end-to-end multimodal AI data projects, managing scope, timelines, budgets, staffing, client communication, workflows, annotation guidelines, quality metrics, and delivery risks. Translates technical requirements into collection and annotation processes, troubleshoots data issues, develops SOPs and QA frameworks, advises on feasibility and tooling, and mentors operations specialists. The role requires experience with AI data operations or project coordination and familiarity with image, video, 3D, sensor, or robotics workflows.

Leads end-to-end multimodal AI data projects, managing scope, timelines, budgets, staffing, risks, client communication, workflows, annotation guidelines, quality metrics, and delivery standards. Advises on data collection, annotation methods, tooling, and feasibility while troubleshooting quality issues and ambiguous requirements. Builds reusable SOPs and QA frameworks, mentors operations specialists, and supports project scoping and pilots involving image, video, 3D, sensor, or robotics data.

Leads end-to-end AI post-training and model evaluation projects, managing scope, timelines, budgets, staffing, risks, client communication, and delivery quality. Translates model improvement goals into data requirements, workflows, rubrics, evaluation plans, and acceptance criteria. Advises on preference data, model comparisons, quality standards, and feasibility; develops SOPs and QA frameworks; resolves ambiguous requirements; mentors operations specialists; and supports project scoping and new capability development.

Leads end-to-end AI post-training and model evaluation projects, managing scope, timelines, budgets, staffing, risks, clients, and delivery quality. Translates model improvement goals into data requirements, workflows, rubrics, evaluation plans, and acceptance criteria. Investigates quality issues, develops SOPs and QA frameworks, advises on data and evaluation design, mentors operations specialists, and supports project scoping and capability development.

Leads Appen’s IT function, including support, enterprise technology, security governance, automation, vendor management and team development. Owns the IT roadmap, Microsoft 365 and SaaS environment, endpoint and identity controls, SOC 2 and ISO 27001 readiness, technology spend, and AI-enabled process improvement. Partners with executives, Engineering and Product to improve service delivery, eliminate manual work, manage risk and build a scalable, high-performing IT organization.

Own enterprise sales cycles targeting frontier AI labs, Big Tech, and enterprise AI adopters. Acquire strategic accounts, expand existing relationships, develop territory plans, manage complex RFPs, negotiate enterprise agreements, and coordinate internal teams across delivery, legal, finance, and client partnerships. Build technical and executive relationships, conduct customer meetings and business reviews, maintain CRM discipline, and deliver accurate forecasts and account growth.

Works directly with enterprise clients to design, deploy, and optimize GenAI data solutions. Translates ambiguous requirements into scalable datasets, annotation guidelines, validation workflows, automation, and observable pipelines. Owns delivered data quality and partners with engineering and client machine learning teams to meet accuracy, reliability, and performance standards. Develops reusable quality frameworks and validators for GenAI model development.

Build practical AI research systems for laboratory and customer use cases. Responsibilities include creating reinforcement learning environments, agentic systems, LLM pipelines, evaluation harnesses, benchmarks, and model-testing workflows. Run fine-tuning and model experiments, deploy local or self-hosted models, document reproducible results, and collaborate with GenAI researchers and cross-functional stakeholders to turn research concepts into reusable applications.

Annotate 2D and 3D objects in imagery, video, and LiDAR point clouds for autonomous-machine perception datasets. Create segmentation, tracking, pose, and keypoint labels while following detailed guidelines and quality standards. Review work, address QA feedback, document tooling and data issues, escalate ambiguities, and contribute to calibration sessions and annotation guideline improvements.

Build and manage strategic client relationships for AI and data projects, serving as the primary stakeholder contact and coordinating internal delivery teams. Define project scope, staffing, pricing, timelines, budgets, and success criteria; manage risks and changes; identify account expansion and renewal opportunities; and lead performance reviews. The role combines project management, strategic consulting, commercial analysis, forecasting, and client advisory responsibilities to support retention, satisfaction, and profitable revenue growth.

Lead strategic legal AI pilots and research projects from scoping through operationalization. Define legal quality, feasibility, cost, workflows, expert requirements, quality standards, and scalable delivery models. Apply legal judgment to case law, contracts, statutes, regulatory filings, and legal reasoning while addressing privilege, confidentiality, and jurisdictional requirements. Partner with customers, researchers, attorneys, legal experts, and global delivery teams to validate and scale successful programs.

Lead strategic finance pilots and research projects supporting financial AI model training and evaluation. Scope initiatives, assess financial data quality and feasibility, define workflows and expert requirements, establish quality standards, and transition validated pilots into scalable operations. Partner with customers, researchers, and global delivery teams while applying expertise in financial data, markets, accounting, disclosures, and quantitative or risk models.

Acquire new strategic enterprise customers and expand high-value accounts across AI labs, Big Tech, and enterprise AI organizations. Own full enterprise sales cycles, develop account plans, manage executive and technical stakeholders, create proposals and business cases, lead RFP responses, negotiate enterprise agreements, coordinate internal teams, maintain CRM discipline, and deliver accurate forecasts. Success is measured by account growth, strategic influence, and pipeline development.