Senior Machine Learning Engineer, Synthetic Data & Document Understanding

Навыки

• Computer Vision
• Решения на данных
• Data Quality
• Лидерство
• Machine Learning
• NLP
• Zapier / Make / n8n

О компании и продукте

• 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 .
• Join ABBYY and be part of a team that celebrates your unique work style. With flexible work options, a supportive team, and rewards that reflect your value, you can focus on what matters most – driving your growth, while fueling ours.

Задачи

• 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

Требования

• 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

Паспорт вакансии

История публикации

Откуда что взялось

Отмечено то, что вывели мы. Без пометки — значение назвал работодатель.

Почему на этом месте в выдаче

Порядок выдачи объявлен контрактом: свежесть решает между днями, полнота и зарплата — внутри дня.