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At Artera, we value bringing together individuals from diverse backgrounds to develop new and innovative solutions for patients and physicians. As an equal opportunity employer, we do not discriminate on the basis of race, color, religion, national origin, age, sex (including pregnancy), physical or mental disability, medical condition, genetic information gender identity or expression, sexual orientation, marital status, protected veteran status, or any other legally protected characteristic.
Machine Learning Engineer (Model Dev)
Location
United States
Posted
76 days ago
Salary
$140K - $180K / year
Seniority
Mid Level
Job Description
Machine Learning Engineer (Model Dev)
Artera
About Us: Artera is an artificial intelligence company dedicated to transforming cancer care. We’ve developed foundation models that analyze clinical and pathology data, generating actionable insights that guide therapy selection and improve outcomes for cancer patients. By continuously improving these models, we aim to uncover the biological mechanisms driving cancer progression. We’re looking for experienced machine learning engineers to work on both product development and core algorithmic research. As a part of the 3rd generation of our core foundation model, you’ll contribute to efforts to identify new approaches to model training that amplify clinically relevant biology while remaining robust across datasets, institutions, and patient populations. You will help design, build, deploy, and continuously improve multimodal models—combining whole-slide images with clinical and molecular data—to predict molecular traits and patient outcomes. Essential Responsibilities: - Develop and evaluate AI-based biomarkers using multimodal data. - Design, implement, and improve machine-learning models to predict patient outcomes and treatment response. - Contribute to the end-to-end model development lifecycle, including data preparation, training, evaluation, and validation. - Support the productionalization, launch, and monitoring of machine-learning models in collaboration with platform and product teams. - Conduct research and experimentation to improve model performance, robustness, generalizability, and interpretability. - Collaborate with biostatistics, clinical, and product partners to translate clinical questions into machine-learning solutions. - Contribute to scientific publications and conference submissions alongside the broader research team. Experience Requirements: - 2+ years of industry experience using PyTorch or TensorFlow. - Experience contributing to machine-learning systems deployed or maintained in production environments. - Ability to clearly communicate complex technical concepts to cross-functional, non-ML collaborators. Desired: - Experience working with large-scale image data or computer vision models. - Familiarity with self-supervised representation learning (e.g., MoCo, DINOv2) and / or vision–language models (VLMs) and multimodal representation learning. - Interest in healthcare, medical imaging, or applied machine learning in regulated or high-impact domains. Here are few posts from our teammates, partners and customer voices to highlight the work we do: - Artera Shapes the Future of Cancer Treatment Using Machine Learning on AWS - How Artera AI test allowed Bruno to avoid hormone therapy - Startups are using AI to predict responses to Cancer Drugs - ArteraAI validates its Prognostic Model in Advanced Prostate Cancer - How Artera Enhances Prostate Cancer Diagnostics Using AWS $140,000 - $180,000 a year In addition to base salary, equity is a core component of our compensation. We also offer 401k matching, unlimited paid time off (PTO), and more. The base salary is competitive and commensurate with experience, qualifications, and other factors to be discussed during the interview process. Equal Employee Opportunity:At Artera, we value bringing together individuals from diverse backgrounds to develop new andinnovative solutions for patients and physicians. As an equal opportunity employer, we do notdiscriminate on the basis of race, color, religion, national origin, age, sex (including pregnancy),physical or mental disability, medical condition, genetic information gender identity orexpression, sexual orientation, marital status, protected veteran status, or any other legallyprotected characteristic.
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