ABBYY
Enterprise · Document AI
- HQ
- Milpitas, US
- Founded
- 1989
- Size
- 500+
- Website
- abbyy.com
ABBYY has 13 active AI-related job listings. The majority of these roles are in data, accounting for 38% of the openings, followed by serving infrastructure at 31%. Engineering is the top function across all these positions. The company is hiring for these roles in Hungary and India. Frequent technology tags include model_serving, inference_infra, and vision, suggesting a focus on deploying and optimizing AI models.
Enterprise · Document AI
Currently tracking 7 active AI roles, down 60% versus the prior 4 weeks. Primary focus: Ship · Engineering. Salary range $75k–$170k (avg $125k).
ABBYY currently has 12 active AI-related roles in our index. The most common open titles are: Senior Software Engineer – C# (2), Senior Software Engineer – C++ (2), Staff Software Engineer (2), Principal Machine Learning Engineer - Model Efficiency Optimization, Principal Machine Learning Engineer, Document AI Data (Tech Lead Manager). Most positions are in Engineering.
ABBYY's active AI hiring is concentrated in: data (42%), serving infrastructure (25%), agents (17%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
ABBYY is hiring AI talent in: Hungary (7 roles), India (5 roles).
Job postings at ABBYY most frequently mention: Machine Learning, Vision-Language Models, Applied Mathematics, Large Language Models (LLMs), Aerospace.
In the past 30 days, ABBYY has posted 0 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Principal Machine Learning Engineer - Model Efficiency Optimization Seeking a Principal Machine Learning Engineer to lead ABBYY's model optimization strategy for document AI at scale. This role involves defining technical direction from research to production, focusing on building efficient, high-performing models. Responsibilities include establishing evaluation frameworks for quality vs. efficiency trade-offs, leading implementation of optimization pipelines, and collaborating with various teams to ensure optimized models meet performance standards. Requires expertise in model optimization techniques, efficient deep learning, VLMs, and Python/PyTorch. | ServePost-train | 9 |
| Senior Machine Learning Engineer, Synthetic Data & Document Understanding This role focuses on building generative pipelines for synthetic data at scale for document understanding tasks. The engineer will design and implement systems to produce high-quality, diverse synthetic training data, develop evaluation frameworks, and ensure the synthetic data improves downstream model performance. Responsibilities include owning the synthetic data generation track end-to-end, driving architectural decisions, and building scalable, cost-efficient pipelines. |
| Data |
| 8 |
| Senior Machine Learning Engineer - AI-Assisted Data Annotation This role focuses on designing, building, and owning AI-assisted data annotation pipelines using LLMs and vision-language models to generate high-quality training data at scale for enterprise customers. It involves developing annotation strategies, quality validation systems, and optimizing inference pipelines. | DataPost-train | 8 |
| Principal Machine Learning Engineer, Document AI Data (Tech Lead Manager) Principal Machine Learning Engineer (Tech Lead Manager) to lead ABBYY’s Document AI Data team. This role involves hands-on technical leadership and people management, owning the architecture and roadmap for building high-quality training data at scale, and managing the team. The focus is on defining how training data is created, validated, and scaled for LLMs and VLMs. | DataPost-train | 8 |
| Senior Machine Learning Engineer, Model Training & Evaluation Senior Machine Learning Engineer responsible for the end-to-end training and evaluation cycle of document AI models, focusing on creating reliable, reproducible, and scalable experimentation pipelines. The role involves designing and maintaining evaluation frameworks, optimizing training processes, and collaborating with platform engineering for infrastructure support. | Post-trainServe | 8 |