Machine Learning Engineer

About the Role

The role's focus on machine learning model development, deployment, and improvement directly impacts clinical workflows and patient outcomes. This is a unique opportunity for a skilled engineer to make a tangible difference in the lives of cancer patients worldwide. Radformation's commitment to using technology to reduce cancer's global impact aligns with the growing demand for digital solutions in healthcare, making this role both challenging and rewarding.

What You Will Do

• Design, build, and maintain robust ETL pipelines to support AI model development and deployment, ensuring seamless data integration and processing.
• Develop, train, and optimize machine learning models used in radiotherapy software, focusing on improving model performance and clinical relevance.
• Collaborate with product and research teams to bring new AI-driven features and algorithms into production, contributing to the continuous innovation of Radformation's products.
• Support FDA submissions by contributing to documentation, validation, and regulatory processes, ensuring compliance with strict medical device software standards.
• Participate in design reviews, risk analyses, and cross-functional discussions to ensure the development of safe and effective products, aligning with Radformation's mission and values.
• Mentor junior engineers and data scientists, contributing to a collaborative team environment that fosters growth and knowledge sharing.
• Engage in the development of convolutional neural networks, including U-Net architectures, and apply hands-on experience with PyTorch and/or TensorFlow to drive project success.
• Utilize Git and modern code repositories (such as GitHub, Bitbucket, Azure DevOps) for version control and collaboration.
• Stay updated with the latest advancements in machine learning and medical imaging, applying this knowledge to improve Radformation's products and services.

What We Are Looking For

• MS in Computer Science, Mathematics, Statistics, or a related field with at least 3 years of experience in machine learning or a related field.
• Expert-level proficiency in Python, with the ability to design, develop, and deploy machine learning models.
• Hands-on experience building, training, and tuning machine learning models, particularly in deep learning frameworks such as PyTorch and TensorFlow.
• Strong experience with convolutional neural networks, including the development and application of U-Net architectures.
• Experience using Git and modern code repositories for collaborative software development.
• Excellent problem-solving skills, with the ability to work independently and as part of a remote team.
• Strong communication and collaboration skills, with experience in mentoring junior team members.
• Familiarity with clinical data standards such as DICOM or HL7, and experience working in regulated environments (HIPAA, FDA, or medical device software) is highly desirable.

Nice to Have

• Experience with medical imaging and image processing techniques, including segmentation, resampling, and smoothing.
• Familiarity with AI-assisted development tools and their application in software development.
• Prior experience working on projects related to healthcare or medical devices, with a understanding of the regulatory landscape.
• Certification in machine learning or a related field, demonstrating a commitment to professional development.

Benefits and Perks

• Competitive compensation package, with a salary range that reflects industry standards for machine learning engineers.
• Opportunity to work on a mission-driven project that has the potential to make a significant impact on global healthcare.
• Comprehensive health and wellness benefits, including multiple high-quality medical plan options with substantial employer contributions.
• Financial and professional growth opportunities, including a 401(k) with employer match, annual reimbursement for professional memberships, and support for conference attendance and continued learning.
• Flexible work environment that fosters work-life balance, with the option to work remotely and contribute to a collaborative, global team.
• Access to cutting-edge technologies and the opportunity to develop skills in AI, machine learning, and medical imaging.

• Tip: Highlight your proficiency in Python and experience with deep learning frameworks like PyTorch and TensorFlow in your application.
• Showcase projects or certifications that demonstrate your understanding of machine learning models, particularly those applied to medical imaging.
• Prepare to discuss your experience with Git and modern code repositories, emphasizing collaboration and version control skills.
• Demonstrate your knowledge of clinical data standards and regulated environments, even if it's not direct experience, by showing an understanding of HIPAA, FDA, or medical device software regulations.
• Be ready to explain how you handle challenges in a remote work environment and how you maintain productivity and collaboration with team members.
• Discuss your approach to continuous learning, especially in the rapidly evolving field of machine learning and AI, and how you stay updated with the latest technologies and methodologies.