Machine Learning Engineer - ML Planning

RaySearch is expanding in the field of machine learning to improve cancer care and is looking for a machine learning engineer to join our head office in Stockholm. Joining our team provides you with a unique opportunity to innovate and develop software that improves the lives of cancer patients around the world.

About The Position

You will be part of an agile team developing innovative solutions using machine learning (ML) algorithms to automate and support the process of improving future cancer treatments.

As a member of the ML Planning team, you will develop deep learning models for medical image analysis to be deployed at cancer clinics, helping them automating their treatment planning process. You will work closely with medical physicists and radiation oncologists and you'll incorporate their domain expertise into model architectures and validation approaches.

Your focus will extend beyond initial development to optimization of model performance, balancing prediction accuracy with clinical deployment requirements. You'll implement rigorous testing and validation frameworks to ensure model reliability in clinical settings, while leading code reviews and maintaining high standards for documentation and reproducibility.

Your profile

We think that you are passionate about machine learning techniques and algorithms with a strong drive to develop and assess innovative solutions.

We're looking for someone with proven experience developing production-grade ML systems using PyTorch or TensorFlow, and expertise in processing and analyzing image data. Experience with ML deployment pipelines, model serving architectures, and ML experiment tracking tools are a plus.

Delivering high quality results is something you take pride in, and you thrive in a dynamic and fast paced environment. You are a strong team player who takes responsibility and contributes to a positive atmosphere and you are passionate about developing software for health care professionals and their patients.

Requirements

• Ph.D. or M.Sc. in computer science/machine learning or equivalent
• Excellent understanding of machine learning techniques and algorithms
• 3+ years’ experience of implementing machine learning solutions
• Strong programming skills in Python
• Experience using deep learning libraries such as TensorFlow or PyTorch
• Solid knowledge of data structures, statistics and applied mathematics
• Experience training deep learning models for computer vision
• Excellent spoken and written communication skills in English
  
  
  

Meriting

• Experience using tools for MLOps
• Experience in healthcare / medtech
  
  
  

Our Culture

Culture at RaySeach is the driving force behind our organization, where everything we do is driven by a shared passion for innovation and the fight against cancer. Our dedication is reflected in our ability to deliver exceptional results, pay close attention to detail, and consistently go the extra mile. Our employees stand out as experts in their field, driven by a relentless focus on solving problems - no matter how complex. At RaySearch, we take pride in leading the way in cancer treatment, leveraging cutting-edge technology to develop innovative solutions that make a real difference in patient care.

Our Offer

At RaySearch, we offer a diverse and inclusive work environment, fostering openness, sincerity, and collaboration. Located in Hagastaden, Stockholm's Life Science Hub, our modern and creative workspace includes an in-house gym, yoga, and social activities like ping pong, table football, and regular after-work events. Our bistro serves a fantastic lunch buffet, and we offer morning- and afternoon-fika every day. Our rooftop terrace also provides a stunning 360-degree view of Stockholm, enhancing the work experience. All of this comes attached with a competitive compensation and benefits package.

Application

Apply for the position through the application form below. Please note that we are approaching the holiday season. A response to your application may be delayed until January.