Senior Machine Learning Engineer

Location: Brooklyn, NY or Bay Area preferredMathpix is looking for a Senior Machine Learning Engineer with deep expertise in computer vision, sequence modeling, and multimodal AI. As a leader on our ML team, you'll play a pivotal role in advancing the state of the art in OCR and related applications, building custom models that push the boundaries of what's possible in text recognition, document understanding, and multimodal learning.The ideal candidate has a PhD in CS, ML, CV, NLP, or a related field, and many years of experience designing, training, and deploying deep learning models at scale. They have worked on sequence-to-sequence models, attention mechanisms, and large multimodal systems, and are motivated by the challenge of building production-grade AI models for mission-critical applications.Responsibilities:Research, design, and implement custom deep learning models for OCR and multimodal document understanding tasksBuild and train sequence-to-sequence and attention-based architectures for text recognition, translation, and generation tasksLead 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 inferenceCollaborate with product and engineering teams to integrate models into production systems, ensuring scalability, robustness, and efficiencyWork 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 featuresMentor junior engineers and provide technical leadership in model architecture, experimentation, and deployment best practicesRequired skills:PhD in Computer Science, Machine Learning, Computer Vision, NLP, or a related field3+ years of hands-on experience in deep learning research and developmentStrong expertise in sequence-to-sequence models, attention mechanisms, and Transformer-based architecturesProven 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 tasksDeep understanding of core ML concepts: optimization, regularization, model scaling, and distributed trainingDemonstrated ability to take models from research to production in a high-stakes environmentNice to have:Experience with large-scale multimodal foundation models and techniques for fine-tuning/adaptationKnowledge of advanced evaluation methodologies for sequence and multimodal modelsPublications in top ML/AI/vision conferences or journals (e.g., NeurIPS, CVPR, ACL, ICML)Experience mentoring teams and driving research agendas in applied AI settingsExperience working at a startup or high-growth company is a strong plus - bonus points if you've been part of a founding or early engineering teamContributions outside of work (personal projects, open-source work, published articles, or blog posts) are a strong plus and speak for themselves