Machine Learning Engineer, Model Development

Artera.net

. Develop and evaluate AI-based biomarkers using multimodal data, including whole-slide images, clinical variables, and molecular data, to predict patient outcomes, treatment benefit, and molecular traits .

Posted 10/8/2026full-timeRemote • United StatesJunior💰 $140,000 - $180,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and evaluating AI-based biomarkers and machine-learning models, with strong capabilities in Python programming and familiarity with oncology and biomarker development. Proven ability to collaborate with cross-functional teams and support regulatory documentation in healthcare environments.

Highest-signal resume keywords

Machine Learning Model DevelopmentDeep Learning with PyTorchOncology and Biomarker DevelopmentModel Evaluation and ValidationCloud-Based ML Development

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

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

Hard Skills

Machine LearningDeep LearningPython ProgrammingModel Evaluation MetricsSelf-Supervised LearningMultiple-Instance LearningSurvival AnalysisData AnalysisExperimental DesignClinical Data Evaluation

Soft Skills

Effective CollaborationClear CommunicationTroubleshooting

Tools & Technologies

PyTorchTensorFlowVersion ControlWorkflow OrchestrationExperiment Tracking

Industry Keywords

AI BiomarkersClinical EndpointsRisk StratificationRegulated Healthcare EnvironmentsFDA 510(k)SaMDCLIA/LDT Validation

Tech Stack

Tools & technologies

CloudPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Develop and evaluate AI-based biomarkers using multimodal data, including whole-slide images, clinical variables, and molecular data, to predict patient outcomes, treatment benefit, and molecular traits
Contribute to the development and evaluation of self-supervised foundation models and downstream machine-learning models, including multiple-instance learning, time-to-event / hazard models, segmentation, and classification
Develop and evaluate methods to improve model robustness and reproducibility across scanners, institutions, staining protocols, and patient populations
Explore and apply interpretability methods to explain model decisions, build clinician trust, and drive actionable model improvements
Build and improve tools and workflows that support efficient, reproducible model development, experimentation, validation, and deployment
Perform rigorous model evaluation and analysis, communicate findings clearly, and document experiments and technical decisions
Collaborate with ML scientists and engineers as well as product, biostatistics, clinical development, and regulatory/quality partners throughout model development and validation
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

Requirements

What you’ll need

1+ years of experience developing machine-learning or deep-learning models using PyTorch (or TensorFlow), including relevant master's or graduate research experience
Familiarity with oncology and biomarker development, including cancer biology and treatment pathways, clinical endpoints, risk stratification, and what makes a biomarker clinically actionable
Experience working with real-world datasets and evaluating machine-learning models using appropriate metrics and validation approaches
Strong Python programming skills and familiarity with modern software-development practices, including version control, testing, and code review
Ability to analyze experimental results, troubleshoot model behavior, and communicate findings clearly
Ability to collaborate effectively with ML engineers, scientists, and cross-functional partners
Experience working with complex clinical datasets, such as medical imaging, multi-omics, longitudinal patient records, or data from clinical studies and multi-institutional cohorts
Familiarity with weakly supervised learning, multiple-instance learning, survival analysis, or related methods
Experience with self-supervised representation learning or foundation models
Familiarity with dataset shift and variation across sites, devices, scanners, or acquisition protocols
Exposure to machine learning in regulated healthcare environments, including SaMD, FDA 510(k)/De Novo, design controls, or CLIA/LDT validation
Research experience through publications, conference presentations, internships, or academic projects
Familiarity with cloud-based ML development, including distributed training, workflow orchestration, experiment tracking, or reproducible pipelines

Benefits

Comp & perks

Equity
401k matching
Unlimited paid time off (PTO)