ABBYY
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. Our commitment to respect, transparency, and simplicity means you can trust us to always choose to do the right thing. As a trusted partner for purpose-built AI and intelligent automation, we solve highly complex problems for our enterprise customers and put their information to work to transform the way they do business. Over 10,000 customers trust ABBYY, including many Fortune 500 ones. You will work on further developing a portfolio already containing client names such as DHL, Johnson & Johnson, FDA, DMV, PwC, KeyBank, Spotify, and H&R BLOCK.
As a Senior Machine Learning Engineer (Model Training & Evaluation) at ABBYY, you will own the end-to-end training and evaluation cycle for our document AI models. Working closely with the Principal Machine Learning Engineer, you will transform research direction into reliable, reproducible, and scalable experimentation pipelines , ensuring model improvements are measurable and production-ready. This role is ideal for engineers who thrive at the intersection of applied ML research and production-grade engineering , combining deep technical expertise with strong experimental rigor. Key Responsibilities Training Pipeline & Experimentation Own the end-to-end training pipeline, including data ingestion, orchestration, checkpointing, and result logging Execute large-scale experiments with strong emphasis on reproducibility and traceability Investigate training instabilities, loss anomalies, and performance gaps, providing structured analysis and hypotheses Implement and validate new optimization techniques and training objectives in collaboration with senior ML leadership Continuously improve pipeline efficiency to reduce iteration time while maintaining experiment quality Manage compute resources across parallel experiments, balancing throughput and cost efficiency Evaluation & Benchmarking Design and maintain comprehensive evaluation and benchmarking frameworks Define clear success metrics across accuracy, latency, memory usage, and domain coverage Build automated evaluation pipelines to detect regressions across model checkpoints Analyze results to identify patterns in model performance and quality trade-offs Partner with Data teams to ensure improvements in training data translate to measurable gains Maintain and evolve benchmarking methodologies aligned with industry best practices Infrastructure & Collaboration Partner with Platform Engineering on distributed training infrastructure and experiment tracking systems Develop internal tooling to support model analysis and research workflows Contribute to team standards around reproducibility, experiment tracking, and documentation Collaborate with Platform teams to support model deployment, optimization, and serving
Education & Experience MS or PhD in Computer Science, Engineering, Mathematics, or related field 5+ years of experience in Machine Learning, Applied AI, or related areas Proven experience training and evaluating large-scale language and/or vision-language models Strong background in building evaluation frameworks and benchmarking systems Experience with model optimization or efficient training techniques Technical Expertise Deep understanding of model optimization and compression (e.g., quantization, pruning) Strong proficiency in Python and PyTorch , including distributed training frameworks (e.g., DeepSpeed, FSDP) Experience managing large-scale training runs (job scheduling, checkpointing, fault tolerance) Expertise in evaluation methodology and benchmark design Experience with experiment tracking and reproducibility practices Familiarity with vision-language model architectures and document AI challenges Leadership & Communication Proven ability to independently own complex technical workstreams Strong collaboration skills in cross-functional, research + engineering environments Rigorous problem-solving approach with focus on root cause analysis Clear and concise communication of technical findings and experimental results Benefits Comprehensive medical, accidental, and life insurance Weekly wellness sessions to support your physical and mental well-being A generous paid time off policy #LI-MM1 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.