Machine Learning Engineer (Model Dev)
Artera
Invite-only right now: save jobs, track applications, build tailored resumesAlready have an account? Log in
- Pay
- $140K to $180K
- a year
- Location
- United States
- Work style
- Remote only
- Job type
- Full time
- Posted
- Oct 8
- 12 days ago
Career path
See where this role leads
Jigup maps the next three moves from a job like this one, with the titles and the skills each step asks for.
About the company
Artera
Hospital & health care · 51 to 200 employees
What you'll do
- 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.
What you 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.
Key skills
machine learningdigital pathologyclinical datamodel-development lifecyclevalidationproduction deploymentrobustnessunderstanding model behavior
Keywords for your resumePro
The words this employer's screening is likely to look for.
This job lists resume keywords
The terms this posting uses, pulled out so you can mirror them in your resume. Jigup Pro members see the list on the job board.
See full description on company page, apply now
Read the full posting here
We're looking for a machine learning engineer to help develop AI biomarkers that improve cancer care. You'll work closely with experienced ML scientists and engineers, as well as clinical, biostatistics, product, and regulatory partners, across the model-development lifecycle, from prototyping and experimentation through validation and production deployment. You'll contribute to challenging problems in medical AI, including building models from digital pathology and clinical data, improving robustness across scanners and sites, understanding model behavior, and advancing our pathology foundation models.