Senior Machine Learning Engineer, Synthetic Data & Document Understanding

We are seeking a Senior Machine Learning Engineer – Synthetic Data & Document Understanding to own the synthetic data generation track within ABBYY’s Document AI Data team.

This role focuses on building generative pipelines that produce high-quality, diverse, and realistic synthetic training data at scale. You will ensure synthetic data meaningfully improves downstream model performance by maintaining strong alignment with real-world document structures, formats, and statistical properties.

This is an ideal role for engineers who combine deep generative modeling expertise with rigorous data quality evaluation and production engineering skills.

Key Responsibilities

Technical Development & Innovation

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

Project Ownership & Leadership

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

Infrastructure & Scale

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