Senior Machine Learning Engineer

Mathpix is looking for a Senior Machine Learning Engineer with deep expertise in computer vision, sequence modeling, and multimodal AI. In this role, you'll advance the state of the art in OCR and related applications, building custom models for text recognition and document understanding.

Responsibilities:

• Research, design, and implement custom deep learning models for OCR and multimodal document understanding tasks
• Build and train sequence-to-sequence and attention-based architectures for text recognition, translation, and generation tasks
• Lead development of multimodal language models that combine vision and text for real-world applications (e.g., image-to-text, document parsing)
• Optimize and extend PyTorch-based training pipelines for large-scale datasets and high-performance inference
• Collaborate with product and engineering teams to integrate models into production systems, ensuring scalability, robustness, and efficiency
• Work closely with the in-house data team to define, generate, and curate high-quality training data, enabling rapid iteration on bug fixes and the development of new features
• Mentor junior engineers and provide technical leadership in model architecture, experimentation, and deployment best practices

Requirements:

• PhD in Computer Science, Machine Learning, Computer Vision, NLP, or a related field
• 3+ years of hands-on experience in deep learning research and development
• Strong expertise in sequence-to-sequence models, attention mechanisms, and Transformer-based architectures
• Proven experience building and training custom models in PyTorch (not using off-the-shelf models)
• Track record of work in one or more of the following areas: machine translation, text generation, speech-to-text, OCR, image captioning, or related multimodal tasks
• Deep understanding of core ML concepts: optimization, regularization, model scaling, and distributed training
• Demonstrated ability to take models from research to production in a high-stakes environment
• Experience with large-scale multimodal foundation models and techniques for fine-tuning/adaptation
• Knowledge of advanced evaluation methodologies for sequence and multimodal models
• Publications in top ML/AI/vision conferences or journals (e.g., NeurIPS, CVPR, ACL, ICML)
• Experience mentoring teams and driving research agendas in applied AI settings
• Experience at a startup or high-growth company; founding/early-team experience is a bonus
• Contributions outside of work — personal projects, open-source, articles, or blog posts