Senior Machine Learning Engineer, Synthetic Data & Document Understanding
Role Overview
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
Key Skills for This Role
Full Job Posting
About the Role
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
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:
- Generative models
- Vision-Language Models (VLMs)
- Synthetic data systems
- Proven experience building and evaluating synthetic data pipelines for ML training
- Strong background in data quality evaluation and statistical analysis
- Technical Expertise
- Deep expertise in Vision-Language Models and document understanding (layout, structure, semantics)
- Strong knowledge of generative modeling for structured and semi-structured data
- Understanding of what makes synthetic data valuable:
- Distributional fidelity
- Diversity
- Realistic noise patterns
- Domain coverage
- Strong programming skills in Python with experience in PyTorch or similar frameworks
- Experience evaluating data quality via automated metrics and downstream model impact
- Familiarity with large-scale data pipelines, cloud environments, and experiment tracking
- Leadership & Communication
- Proven ability to independently own complex technical workstreams
- Strong collaboration across data, modeling, and platform teams
- Ability to clearly communicate data quality and generation trade-offs
- Data-driven mindset with strong attention to coverage gaps and quality signals
- Here are some of our local benefits:
- Comprehensive medical, accidental, and life insurance
- Weekly wellness sessions to support your physical and mental well-being
- A generous paid time off policy
- Join ABBYY, and you will:
- Love how you work
- We provide remote and hybrid working options to fit all lifestyles.
- We use flexible hours across most of our teams to allow you to find your own definition of balance.
- Encouraging a culture of giving, we provide two paid volunteering days off every year so you can take time to contribute to the causes you care about.
- To ensure your family is cared for, we offer paid parental leave in all our locations.
- Love whom you work with
- We are a global team of 600+ colleagues, spread across 15 countries on four continents.
- With colleagues representing 30+ nationalities, our workforce reflects the world.
- Innovation and excellence run through our veins. Our teams gather the expertise which has garnered ABBYY more than 140 technology patents.
- We are guided by the values of respect, transparency, and simplicity.
- "Team Environment" is in the top three highest-scoring drivers of engagement across all of our departments.
- Love what you work on
- We are a company with more than 35 years of experience in the technology market;
- Over 10,000 customers trust ABBYY, including many Fortune 500 ones, with names such as DHL, Johnson & Johnson, FDA, DMV, PwC, KeyBank, Spotify, and H&R BLOCK;
- We have modernized the capture market by creating the first low-code/no-code IDP platform.
- Our Machine Learning, Natural Language Processing, Computer Vision Technologies, and a marketplace built with AI, can transform any document in any process;
- Top Analyst firms recognize ABBYY's market leadership, including Gartner, Everest PEAK Matrix ® Assessment, ISG Intelligent Automation Lens, and NelsonHall, amongst others.
- ABBYY is an Equal Employment Opportunity employer that values the strength that diversity brings to the workplace. To learn more about our commitment to Diversity and Inclusion, check out the careers section on our website.
About ABBYY
ABBYY is an enterprise AI company founded in 1989 that develops intelligent document processing, process mining, and task mining solutions used by over 10,000 customers including many Fortune 500 companies.
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