Machine Learning Engineer (Model Development)
New
A
ArteraMedical AI
Remote-USFull-TimeJunior
Salary$140,000 - $180,000 a year
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Job Details
- Experience
- 1+ years of experience developing machine-learning or deep-learning models using PyTorch (or TensorFlow), including relevant master's or graduate research experience.
- Required Skills
- PythonMachine LearningPyTorchTensorflowDeep Learning
Requirements
- Have 1+ years of experience developing machine-learning or deep-learning models using PyTorch or TensorFlow, including relevant master's or graduate research experience.
- Be familiar with oncology and biomarker development, including cancer biology, treatment pathways, clinical endpoints, risk stratification, and clinically actionable biomarkers.
- Have experience working with real-world datasets and evaluating machine-learning models using appropriate metrics and validation approaches.
- Have strong Python programming skills.
- Be familiar with version control, testing, and code review.
- Be able to analyze experimental results and troubleshoot model behavior.
- Be able to communicate findings clearly and collaborate with ML engineers, scientists, and cross-functional partners.
- Experience with complex clinical datasets such as medical imaging, multi-omics, longitudinal patient records, or clinical-study and multi-institutional cohort data is desired.
- Familiarity with weakly supervised learning, multiple-instance learning, survival analysis, or related methods is desired.
- Experience with self-supervised representation learning or foundation models is desired.
- Familiarity with dataset shift and variation across sites, devices, scanners, or acquisition protocols is desired.
- Exposure to regulated healthcare machine learning, including SaMD, FDA 510(k)/De Novo, design controls, or CLIA/LDT validation, is desired.
- Research experience through publications, conference presentations, internships, or academic projects is desired.
- Familiarity with cloud-based ML development, including distributed training, workflow orchestration, experiment tracking, or reproducible pipelines, is desired.
Responsibilities
- Develop and evaluate AI-based biomarkers using whole-slide images, clinical variables, and molecular data to predict patient outcomes, treatment benefit, and molecular traits.
- Contribute to self-supervised foundation models and downstream models, including multiple-instance learning, time-to-event or hazard models, segmentation, and classification.
- Develop and evaluate methods to improve model robustness and reproducibility across scanners, institutions, staining protocols, and patient populations.
- Explore interpretability methods to explain model decisions and support clinician trust and actionable model improvements.
- Build and improve tools and workflows for reproducible model development, experimentation, validation, and deployment.
- Perform model evaluation and analysis, communicate findings, and document experiments and technical decisions.
- Collaborate with ML scientists and engineers and with product, biostatistics, clinical development, and regulatory and quality partners.
- Support regulatory and quality documentation related to AI model development and validation.
- Contribute to peer-reviewed publications, conference presentations, and external academic or industry collaborations.
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