Synthetica helps maritime operators turn data into actionable insights to improve efficiency and reduce costs. It delivers AI-powered software products—real-time vessel monitoring (IEM), anomaly…
Synthetica helps maritime operators turn data into actionable insights to improve efficiency and reduce costs. It delivers AI-powered software products—real-time vessel monitoring (IEM), anomaly…
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About Synthetica
Synthetica is a document automation and workflow intelligence company specializing in AI-driven solutions for processing, understanding, and automating complex document pipelines. We build cutting-edge models that reason over images, language, layout, and structured data — and we’re looking for an AI Engineer to help design, train, evaluate, and deploy next-generation document intelligence systems.
Requirements
Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or related STEM field
Strong understanding of machine learning and deep learning concepts
Experience with Python and modern ML frameworks such as PyTorch or TensorFlow
Familiarity with document AI systems, OCR pipelines, and multimodal architectures
Experience working with model training, inference, evaluation, and serving workflows
Understanding of computer vision, NLP, and Vision-Language Models (VLMs)
Familiarity with model training coding pipelines and experimentation workflows
Strong ability to follow engineering standards and maintain high-quality implementations
Excellent attention to detail and problem-solving skills
Ability to work independently and meet deadlines
Strong communication and collaboration skills
Familiarity with Git and version control workflows
Responsibilities
Nice to Have
Prior experience in document AI, multimodal systems, or related machine learning engineering roles
Experience with document-focused architectures such as LayoutLM, Donut, Nougat, or DocFormer
Experience in model training workflows, dataset curation, or evaluation system design
Familiarity with OCR frameworks and document processing toolchains
Interest in building and benchmarking AI evaluation frameworks and quality metrics
Experience with synthetic data generation or augmentation pipelines
Familiarity with inference optimization and model serving infrastructure
Benefits
Competitive compensation & ticket restaurant card
Flexible working schedule & extensive insurance plan
Cutting-edge IT equipment and continuous training programs
Coding assistants provisioning
Build and optimize document intelligence systems spanning image, language, layout, and reasoning tasks
Train, fine-tune, evaluate, and serve AI models for document processing and workflow automation applications
Work across multiple document modeling architectures including convolutional, recurrent, transformer-based, and Vision-Language Models (VLMs)
Develop hybrid multimodal pipelines combining vision, text, layout, OCR, and structured data representations
Create and maintain scalable synthetic data pipelines to generate high-quality document corpora for model training
Design and maintain training, inference, benchmarking, and evaluation workflows for document AI systems
Build and optimize serving pipelines for production-grade document understanding platforms
Tag and annotate document elements — form fields, tables, text regions, layout structures, and semantic entities — across scanned and digital documents
Precise Data Labeling: Identifying and tagging document components and visual structures across multimodal datasets
Quality Assurance: Reviewing and correcting annotations, datasets, and model outputs to ensure high accuracy and consistency
Edge Case Identification: Spotting ambiguous document structures and failure cases to improve model robustness and evaluation quality
Feedback Loop Collaboration: Working closely with ML engineers and researchers to refine annotation guidelines, model behavior, and evaluation strategies
Maintain documentation for annotation schemas, model training workflows, evaluation pipelines, and evolving document processing requirements
Experience with SQL or distributed data processing systems
Interest in AI, machine learning, and document understanding research
