Design, build, and maintain robust ETL pipelines to support AI model development and deployment.
Develop, train, and optimize machine learning models used in radiotherapy software.
Collaborate with product and research teams to bring new AI-driven features and algorithms into production.
Support FDA submissions by contributing to documentation, validation, and regulatory processes.
Participate in design reviews, risk analyses, and cross-functional discussions to ensure safe and effective products.
Mentor junior engineers and data scientists and contribute to a collaborative team environment.

Requirements

MS in Computer Science, Mathematics, Statistics, or a related field with 3+ years of experience.
Expert-level proficiency in Python.
Hands-on experience building, training, and tuning machine learning models.
Strong experience with PyTorch and/or TensorFlow.
Experience developing convolutional neural networks, including U-Net architectures.
Experience using Git and modern code repositories (GitHub, Bitbucket, Azure DevOps, etc.).

Benefits

Multiple high-quality medical plan options with substantial employer contributions toward premiums, often covering the full cost depending on the plan selected.
Health coverage starting on day one
Short-term and long-term disability and supplementary life insurance
401(k) with employer match vested immediately
Annual reimbursement for professional memberships
Conference attendance and continued learning opportunities
Self-managed PTO and 10 paid holidays
Monthly internet stipend
Company-issued laptop and one-time home office setup stipend
Fully remote work environment with virtual events and yearly retreats, because we like to have fun while doing work that matters

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

ETL pipelinesmachine learningPythonPyTorchTensorFlowconvolutional neural networksU-Net architecturesGitGitHubBitbucket

Soft Skills

collaborationmentoringteam environmentcommunicationrisk analysis