Senior Machine Learning Engineer
vor 4 Wochen
About BLP Digital
We are a rapidly growing Zurich-based software company (official ETH and HSG spin-off), revolutionising ERP automation with our leading machine learning solution. In the past 12 months, we have grown from 12 to over 70 passionate team members and centrealised our Swiss teams into one HQ on the iconic Zürich Bahnhofstrasse. As a fully self-financed company, such growth is only possible due to our strong, solid commercial success. To date, nearly 300 companies (including several Fortune 500 companies) rely on our platform.
About ML at BLP
At BLP Digital, we develop a cutting-edge product for automated document processing. Machine learning is at the core of our business, and we continuously strive to enhance the quality of our predictions using state-of-the-art machine learning models. Working in a startup presents exciting challenges, such as scaling both our team size and the processing volume to accommodate growing demands. Throughout this journey, we prioritize a simple and maintainable architecture to ensure efficient operations. To stay at the forefront of technology, we leverage a modern tech stack that includes Python, Docker, TensorFlow, Redis, RabbitMQ, and Micro Services on a Kubernetes Cluster, deployed as Software-as-a-Service on one of the big cloud providers. In our team, we value the opportunity for our engineers to shape the future and have a significant impact on various aspects. This influence spans areas such as organizational structure, the coding process, team culture and organization.
Location: This is a full-time position with on-site presence at our office in central Zurich, Switzerland.
Key Responsibilities:
Software Development:
Design and develop clean and maintainable software solutions using Python, leveraging industry best practices and design patterns, ensuring high code quality.
Conduct code reviews and provide constructive feedback to ensure the overall quality and consistency of the codebase.
Machine Learning:
Bridge the gap between the business case and the technical solution to solve it, while focusing on choosing the right tool for the job.
Develop and maintain custom transformer-based NLP architectures.
Understand and reason about state-of-the-art research, extract relevant ideas from it and use it to improve our models.
Work on all aspects of the end-to-end prediction service, including data-management, model improvements, monitoring and scalability
Requirements:
Masters or PhD Degree in Data Science/Computer Science with a focus on ML or equivalent practical experience.
Typically 5+ years of experience with Machine Learning algorithms in either an academic or industry setting.
Structured and self-reliant approach.
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