Machine Learning Engineer
Metamaze is a product scale-up specialized in Intelligent Document Processing. Our mission is to extract any type of information from any kind of document.
We extensively train and serve custom Transformers-based models (800+ models and growing fast).
Metamaze is a product scale-up specialized in Intelligent Document Processing. Our mission is to extract any type of information from any kind of document. We extensively train and serve custom Transformers-based models (800+ models and growing fast).
What will you do?
We are seeking a talented Machine Learning Engineer to join our dynamic team. As a key engineering team member, you will play a crucial role in developing and enhancing all ML components of our Intelligent Document Processing platform.
You will have the opportunity to work on cutting-edge NLP techniques and leverage recent advancements in the field to solve complex document processing challenges.
Models we support include entity extraction, cross-chunk relation extraction, nested NER, multi-label classification, sentence classification, similarity-based retrieval, outlier detection, clustering, OCR, object detection, … You will work with interesting, real-world datasets at scale.
- Collaborate with the ML, Full Stack, and product teams to discuss requirements for the development process.
- Optimize and fine-tune existing models to improve accuracy, performance, and scalability as well as implement new machine learning algorithms and models for Intelligent Document Processing.
- Stay informed about the latest advancements in NLP, LLMs, and document processing. Identify the right tooling and frameworks to integrate them into our platform.
- Work on data validation, data preprocessing, and model evaluation to ensure high-quality output.
- Document technical specifications, experiments, and findings to support knowledge sharing within the team. Report on findings in blog posts or publications.
- Actively contribute to and brainstorm about the product roadmap with regards to the integration of machine learning.
Who are you?
If you are passionate about solving challenging problems and want to be at the forefront of innovation, we would love to hear from you!
To apply, send an e-mail to firstname.lastname@example.org containing your resume, a short cover letter, and any relevant code samples you have
Selected resumes will be invited for an initial in-take call (5-10 min), a technical interview and finally a general interview.