ABBYY

Human Resources

• Lead, coach, and develop a small, globally distributed HR Operations team
• Drive consistency in service delivery, processes, and standards across regions
• Balance global standardization with local compliance requirements
• Establish clear SLAs, priorities, and performance expectations
• Act as the primary escalation point for complex and cross-regional issues
• Own and operate HR Operations for the U.S. employee population, ensuring full compliance with federal, state, and local requirements
• Provide leadership and oversight to global HR operations delivery, ensuring alignment and consistency across regions
• Drive end-to-end excellence across the full employee lifecycle globally, including onboarding and hiring support, job and compensation changes, leaves of absence, offboarding

HR Operations Manager

Product Manager

• Partner with product managers and key stakeholders to define and execute the strategic roadmap for AI-powered products and features, aligning with company objectives and customer needs
• Prioritize investments and initiatives that drive business growth and innovation
• Lead market analysis, customer research, and competitive assessments to identify opportunities for AI integration and differentiation
• Stay informed on emerging AI technologies to inform product direction
• Collaborate with engineering, design, data science, and cross-functional teams to design, develop, and launch AI-driven solutions
• Own product requirements including user stories, technical specifications, success metrics, and go-to-market strategies
• Own and evolve core AI/ML services leveraged across multiple product lines
• Ensure seamless integration of AI capabilities into enterprise workflows
• Evangelize AI initiatives both internally and externally, clearly articulating value, risks, and opportunities
• Educate stakeholders and leadership on AI strategy and business impact
• Leverage experimentation, analytics, and customer insights to continuously improve product performance
• Define and track key metrics such as model accuracy, inference latency, and infrastructure cost optimization
• Operate with a strong focus on measurable outcomes, iteration, and impact

Senior Product Manager, AI

Machine Learning Engineer

• Design and implement pipelines that analyze real documents to inform high-fidelity synthetic data generation
• Build generative systems capable of producing documents across diverse formats, layouts, and domains
• Develop evaluation frameworks to ensure synthetic data maintains distributional fidelity and diversity
• Research and apply generative modeling techniques suited for document AI training
• Identify and mitigate quality issues to ensure synthetic data is effective for downstream model training
• Partner with Modeling teams to measure the impact of synthetic data on model performance
• Own the synthetic data generation track end-to-end, from architecture to quality validation
• Drive architectural decisions balancing quality, diversity, scale, and cost efficiency
• Define and maintain data quality metrics and generation dashboards
• Collaborate closely with annotation teams to ensure compatibility with downstream pipelines
• Contribute to roadmap planning alongside Principal-level leadership
• Build scalable pipelines capable of generating millions of synthetic training examples
• Implement post-processing, filtering, and validation mechanisms to remove low-quality outputs
• Design cost-efficient workflows balancing compute, quality, and throughput
• Develop monitoring systems to detect distribution shifts or quality degradation over time
• Collaborate with Platform teams on compute orchestration, storage, and scheduling.

Senior Machine Learning Engineer, Synthetic Data, Document Understanding

• Own the end-to-end training pipeline, including data ingestion, orchestration, checkpointing, and result logging
• Execute large-scale experiments with strong emphasis on reproducibility and traceability
• Investigate training instabilities, loss anomalies, and performance gaps, providing structured analysis and hypotheses
• Implement and validate new optimization techniques and training objectives in collaboration with senior ML leadership
• Continuously improve pipeline efficiency to reduce iteration time while maintaining experiment quality
• Manage compute resources across parallel experiments, balancing throughput and cost efficiency
• Design and maintain comprehensive evaluation and benchmarking frameworks
• Define clear success metrics across accuracy, latency, memory usage, and domain coverage
• Build automated evaluation pipelines to detect regressions across model checkpoints
• Analyze results to identify patterns in model performance and quality trade-offs
• Partner with Data teams to ensure improvements in training data translate to measurable gains
• Maintain and evolve benchmarking methodologies aligned with industry best practices
• Partner with Platform Engineering on distributed training infrastructure and experiment tracking systems
• Develop internal tooling to support model analysis and research workflows
• Contribute to team standards around reproducibility, experiment tracking, and documentation
• Collaborate with Platform teams to support model deployment, optimization, and serving.

Senior Machine Learning Engineer, Model Training & Evaluation

Data Engineer

• Design and build scalable data pipelines that collect, transform, and deliver structured and unstructured data from internal and external sources.
• Partner with business and technology teams to define data requirements for AI use cases and operational workflows.
• Develop data models, integrations, and data quality controls to support analytics, automation, and model performance.
• Ensure data is available, reliable, secure, and properly governed across systems and environments.
• Work closely with AI engineers, product owners, and process teams to prepare data for experimentation and production use.
• Monitor pipeline health, troubleshoot issues, and continuously improve performance, reliability, and maintainability.
• Support metadata, lineage, and documentation practices that improve data transparency and trust.

HUF 20,000,000 - HUF 26,000,000 per year

Engineer

• Assess current business processes, workflows, handoffs, and pain points across functions.
• Identify opportunities to improve efficiency, quality, cycle time, and customer or employee experience.
• Partner with business leaders to redesign processes for AI-enabled and automation-supported execution.
• Document future-state workflows, operating procedures, controls, and business rules.
• Define process metrics and success measures to track adoption and performance improvements.
• Collaborate with data, technology, and AI teams to ensure process design aligns with implementation requirements
• Support pilot execution, change management, and rollout planning for new ways of working.
• Facilitate workshops and stakeholder sessions to gather requirements and build alignment.

Process Engineer

HUF 24,000,000 - HUF 32,000,000 per year

• Own and operate HR Operations for the U.S. employee population, partnering closely with the Director of Talent Acquisition & HR Operations.
• Drive consistent execution of global HR policies, procedures, and workflows, ensuring compliance with U.S. federal, state, and local employment regulations.
• Serve as the senior escalation point for complex employee and manager inquiries, delivering clear, compliant, and pragmatic guidance.
• Own end-to-end HR lifecycle processes, including onboarding, job and compensation changes, leaves of absence, and offboarding.
• Ensure data accuracy, confidentiality, and compliance across all HR systems of record.
• Lead cross-functional execution with Payroll, Legal, Finance, and IT to ensure coordinated, timely, and compliant HR operations throughout the employee lifecycle.

• Provide regional People Operations support for an assigned geography within the global People Shared Services team.
• Ensure global People policies and workflows are applied appropriately within the region, accounting for local compliance requirements.
• Respond to employee and manager inquiries through our case management system (AskABBYY), meeting all SLAs with timely, accurate, and high-quality support.
• Process global HR lifecycle transactions, including onboarding, job changes, compensation adjustments, leaves, and offboarding.
• Maintain employee data in our People systems of record through validation, updates, and reconciliations.
• Partner cross-functionally to ensure coordinated employee lifecycle support.
• Support configuration and ongoing management of HiBob (HRIS) and Greenhouse (ATS), including workflows, permissions, automations, custom fields, templates, and lifecycle processes.
• Translate business requirements into technical system changes, implementing enhancements and new features as needed.
• Troubleshoot system issues and partner with internal teams or vendors to resolve them.
• Serve as a knowledgeable resource on system functionality, best practices, and platform capabilities.
• Ensure accuracy and integrity of employee data across all People systems.
• Conduct data audits and reconciliations and resolve discrepancies.
• Support the creation and maintenance of scheduled and ad hoc People data reports and dashboards.
• Address data integration issues between People systems and downstream systems such as payroll, IT, or Finance tools.
• Create and maintain SOPs, workflows, system documentation, and user guides.
• Document system configurations and data processes to ensure clarity and consistency.
• Identify opportunities for process improvement, automation, and standardization across regions.
• Ensure all documentation reflects current policies, systems, and operational practices.

HR Operations Manager

Senior Product Manager, AI

Senior Machine Learning Engineer, Synthetic Data, Document Understanding

Senior Machine Learning Engineer, Model Training & Evaluation

Data Engineer

Process Engineer

HR Operations Manager

HR Operations Specialist