Reconstruction Algorithm Devleloper

Job Title

Reconstruction Algorithm Devleloper

Job Description

Reconstruction Algorithm Developer

 

In this role, you are part of

 

The MR Recon Software team, a key part of the MR Domain SW department in MR R&D within Philips. MR R&D focuses on developing and enhancing MR products and solutions. We have defined aspirational goals and a “North Star” in line with customers’ most pressing needs. We work on improving (a) Speed, to ensure predictable and reduced cost per exam; (b) Comfort, to improve patient and staff satisfaction; and (c) Confidence, to support first-time-right diagnosis.

The MR Recon team works on new AI technologies and GPU implementations and is exploring cloud technology to meet future clinical needs. Team members have opportunities to grow as domain specialists or technical specialists.

 

Your Role:

 

• Design, develop, test, and maintain the MR Reconstruction software platform.
• Understand requirements from an end-user perspective and translate them into designs.
• Investigate and resolve customer complaints.
• Develop your skills by working alongside other team members.
• Own and deliver assigned modules as part of the team.
• Support project estimation, planning, and risk management activities.
• Analyze reconstruction algorithms and implementations to identify and deliver performance improvements through algorithmic complexity reduction
• Optimize AI/ML components integrated into the Recon pipeline, including PyTorch-based models, ONNX, TensorRT and CUDA/cuDNN
• Work closely with other reconstruction algorithm developers, AI/ML, software, and systems teams to translate computationally intensive algorithms into efficient, scalable implementations

 

You're the Right Fit If:

 

• MTech or PhD in Computer Science or a related field. An advanced degree is highly preferred.
• 5+ years of experience in software development and design.
• Expert-level experience in C++ and object-oriented design patterns.
• Experience working on large-scale software projects.
• Experience in medical devices or healthcare is preferred; prior experience in the healthcare domain is a bonus.
• Knowledge of Waterfall and Agile development methodologies.
• Clear and coherent written communication skills, including the ability to present data visually.
• Superior interpersonal communication skills.
• Experience analyzing and optimizing computationally intensive reconstruction algorithms and software implementations.
• Experience applying profiling and performance-analysis techniques to identify bottlenecks and deliver measurable improvements.
• Good to have: experience with AI/ML components and tools such as PyTorch, ONNX, TensorRT, and CUDA/cuDNN.
• Ability to improve end-to-end performance while maintaining numerical accuracy and image quality.
• Ability to collaborate effectively across algorithm, AI/ML, software, and systems teams.

 

Minimum required Education and Experience:

MTech with 5+ years of experience, or PhD with 2+ years of experience.

 

 

How we work together:

We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week.

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• Onsite roles require full-time presence in the company’s facilities.
• Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations.
• This role is an office-based role.
  
  

About Philips:
We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.
• Learn more about our business.
• Discover our rich and exciting history.
• Learn more about our purpose.
If you’re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care here.

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