MLOps Engineer

Imagine yourself working at a company that improves breast cancer survival rates.
ScreenPoint is a rapidly growing company that develops and markets leading breast image analysis and cutting-edge machine learning applications and services. Our product, Transpara, is commercialized and used worldwide and is aimed at improving breast cancer survival rates by detecting cancers earlier so that treatment can be more effective and less invasive.
We are looking for an enthusiastic and skilled MLOps Engineer to strengthen the team.

Who we are looking for

We are looking for a highly motivated and ambitious MLOPs Engineer whose responsibility it will be to ensure that the ML methods developed at ScreenPoint can be brought to production in a rapid, reproducible and optimal way. To this end you will need to apply MLOPs principles to streamline the process from incoming data to model deployment. This will enable quick and consistent releases of our latest technological advancements so that our algorithms remain first in class and continue to improve the early detection of cancer for women worldwide.
You’ll be part of a highly motivated and ambitious group of experts in breast imaging, artificial intelligence, software engineering and clinical research who work closely together in an informal atmosphere to make our mission become a reality. You will work with a broad range of technologies and have the opportunity to learn and grow on a daily basis.

What knowledge and expertise you can bring

Required qualifications
• Master’s or PhDs degree in Computer Science, Mathematics, Physics or related field;
• At least 2 years of professional experience in AI development experience or closely related field;
• Experience deploying CI/CD or MLOps pipelines in a professional environment;
• Driven to continuously and iteratively streamline the process from incoming data and model design to model deployment and all the steps involved;
• Experience with model optimization techniques such as quantization, pruning;
• Excellent understanding of running deep learning experiments on GPU clusters and making optimal use of available resources;
• Self-motivated with a high level of energy, enthusiasm and initiative to make the company succeed;
• Driven to constantly identify new ways to improve workflows and try out new tools or developments;
• Excellent and effective all-round communication in English;
• Driven to work in a clean coding environment and help others follow best practices.

Preferred qualifications
• Experience in working with Linux, PyTorch;
• Solid understanding of Python programming and open to learn other languages;
• Experience with Cloud deployment of deep learning experiments;
• Understanding of DICOM standards and medical imaging formats;
• Experience in working with databases (SQL, postgreSQL, noSQL);
• Experience with building dashboards for display using Grafana.

Why would you apply?

• Help millions of women worldwide with our technology.
• Direct exposure to a multidisciplinary team of very smart and passionate experts that are an inspiration to help you develop your career further in many aspects: AI development, software engineering, quality assurance, clinical research, and leadership.
• A competitive salary 
• The possibility to work 32, 36 or 40 hours per week.
• Flexible working hours and hybrid model of remote work.

Where will you work?

ScreenPoint has a flexible and hybrid way of working. Depending on the needs of the team, your location and team planning you will be able to spend a few days per week or month at the office as well as working remotely from home. Our office is located at Toernooiveld 300, Mercator II, in Nijmegen, a minutes’ walk from Nijmegen-Heyendaal station.

How to apply

it-8 is responsible for this vacancy. Please send your application to Niki Huntjens (n.huntjens@it-8.nl), or call/app Niki for more information (06-33940387).

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  • Accepted file types: pdf, doc, docx, Max. file size: 5 MB.
    Accepted file types are limited to .pdf and .doc only. Files sizes must be smaller than 5MB

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