Senior Machine Learning Engineer - AI-Assisted Data Annotation

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