About the Role

We are seeking a Senior Machine Learning Engineer – AI-Assisted Data Annotation to own the automated annotation track within ABBYY's Document AI Data team.

This role sits at the intersection of large model capabilities and production data engineering, leveraging LLMs and vision-language models to generate high-quality training data at scale. You will design and build AI-assisted annotation pipelines, ensuring outputs are accurate, measurable, and reliable for downstream model training.

This is an ideal role for engineers who combine deep model expertise with strong system-building instincts and thrive in fast-moving, experimental environments.

Key Responsibilities

Technical Development & Innovation

Design and implement AI-powered annotation pipelines using large models to generate ground truth labels at scale
Develop and refine 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 which tasks are suitable for automated annotation vs. human review, and define decision criteria
Create evaluation frameworks to benchmark automated annotations against human-labeled data
Continuously improve annotation quality using feedback from human review workflows

Project Ownership & Leadership

Own the automated annotation track end-to-end, from architecture through production monitoring
Drive technical decisions across model selection, pipeline design, and validation strategies
Define integration points with platform infrastructure and model serving systems
Collaborate with Data Operations to design human-in-the-loop workflows for efficient review
Contribute to roadmap planning with Principal-level technical leadership

Infrastructure & Scale

Build and optimize large-scale inference pipelines for processing millions of documents
Implement monitoring and alerting for quality degradation and system failures
Design batching, caching, and fallback mechanisms to balance cost, throughput, and accuracy
Collaborate with Platform teams on model serving, APIs, and infrastructure scaling
Maintain clear documentation of annotation strategies, metrics, and known limitations

Qualifications

Education & Experience

MS or PhD in Computer Science, Engineering, Mathematics, or related field
5+ years of experience in Machine Learning / AI, with focus on:
Large Language Models (LLMs)
Vision-Language Models (VLMs)
Data annotation or labeling systems
Demonstrated success using large AI models to automate annotation at production scale
Strong background in evaluation design and quality measurement

Technical Expertise

Deep expertise in LLMs and VLMs, including prompting, instruction tuning, and output evaluation
Strong understanding of document understanding tasks (classification, extraction, layout analysis, semantic parsing)
Experience designing label quality metrics, confidence scoring, and agreement analysis
Strong programming skills in Python and proficiency with PyTorch or similar frameworks
Experience with large-scale inference pipelines and model serving systems
Familiarity with human-in-the-loop annotation systems and automation trade-offs

Senior Machine Learning Engineer AI Assisted Data Annotation