Senior Machine Learning Engineer - AI-Assisted Data Annotation

About the company ABBYY is a trusted partner for purpose-built AI and intelligent automation with over 35 years in the technology market. They serve more than 10,000 enterprise customers including DHL, Johnson & Johnson, PwC, Spotify, and many Fortune 500 companies. Their platform covers IDP, ML, NLP, and Computer Vision, recognized as a market leader by Gartner and other top analyst firms. About the role This Senior ML Engineer role owns the automated annotation track within ABBYY's Document AI Data team, sitting at the intersection of large model capabilities and production data engineering. The engineer will design and build AI-assisted annotation pipelines leveraging LLMs and vision-language models to generate high-quality training data at scale. It's best suited for engineers who combine deep model expertise with strong system-building instincts in fast-moving, experimental environments. Responsibilities - Design and implement AI-powered annotation pipelines using large models to generate ground truth labels at scale - Develop prompting strategies, few-shot examples, and fine-tuning approaches to improve accuracy and consistency - Build systems for label verification, confidence scoring, and quality validation - Evaluate task suitability for automated annotation vs. human review and define decision criteria - Create evaluation frameworks to benchmark automated annotations against human-labeled data - Own the automated annotation track end-to-end, from architecture through production monitoring - Drive technical decisions across model selection, pipeline design, and validation strategies - Collaborate with Data Operations to design human-in-the-loop workflows - Build and optimize large-scale inference pipelines for processing millions of documents - Implement monitoring and alerting for quality degradation and system failures Requirements - MS or PhD in Computer Science, Engineering, Mathematics, or related field - 5+ years of ML/AI experience with focus on LLMs, VLMs, and data annotation systems - Deep expertise in prompting, instruction tuning, and output evaluation - Strong understanding of document understanding tasks (classification, extraction, layout analysis) - Strong Python programming skills and proficiency with PyTorch or similar frameworks - Experience with large-scale inference pipelines and model serving systems - Proven ability to independently own complex technical workstreams Nice to have - Familiarity with human-in-the-loop annotation systems and automation trade-offs - Experience with batching, caching, and fallback mechanisms for cost/throughput optimization Benefits & perks - Comprehensive medical, accidental, and life insurance - Weekly wellness sessions for physical and mental well-being - Generous paid time off policy - Two paid volunteering days per year - Paid parental leave - Remote and hybrid working options with flexible hours Compensation Not specified