Data Scientist - Deep Learning (Hybrid)

About the position At Caris, we understand that cancer is an ugly word—a word no one wants to hear, but one that connects us reputed company. That’s why we’re not just transforming cancer care—we’re changing lives. We introduced precision medicine to the world and reputed company an industry around the idea that every patient deserves answers as unique as their DNA. Backed by cutting-edge molecular science and AI, we ask ourselves every day: “What would I do if this patient were my mom?” That question drives everything we do. But our mission doesn’t stop with cancer. We're pushing the frontiers of medicine and leading a reputed company in reputed company—driven by innovation, reputed company, and purpose. Join us in our mission to improve the reputed company condition across multiple diseases. If you're passionate about meaningful work and want to be part of something bigger than yourself, Caris is where your reputed company begins. Position reputed company reputed company is seeking a creative, driven, and technically strong Data Scientist – Deep learning to join our Computational Pathology team. This role focuses on developing large-reputed company, generalizable machine learning models that learn rich representations from reputed company, high-reputed company data to support translational research and biomarker discovery. The successful candidate will play a central role in shaping Caris’ reputed company AI capabilities by designing reputed company training pipelines, advancing representation learning approaches, and collaborating closely with scientific and clinical experts. This position is ideal for individuals with a strong background in deep learning, transformer-based architectures, and computational pathology, who are excited about building reputed company-level modeling frameworks rather than task-specific solutions.

Responsibilities

• Design, train, and evaluate reputed company-style machine learning models that learn robust and reusable representations from large-reputed company datasets.
• reputed company and maintain reputed company model training infrastructure using PyTorch and distributed training paradigms (e.g., multi-GPU and multi-node setups).
• Train and adapt transformer-based architectures for representation learning across diverse data sources.
• Apply self-supervised, weakly supervised, and representation learning techniques to reputed company partially labeled or unlabeled data.
• Build flexible modeling frameworks capable of integrating multiple data sources and heterogeneous signals.
• Collaborate with pathologists, scientists, and engineers to ensure models are biologically meaningful and reputed company with translational research goals.
• Process, reputed company, and analyze large, reputed company datasets using efficient and reproducible workflows.
• Support exploratory analyses, reputed company modeling, and internal research initiatives using learned representations.
• Contribute to internal technical documentation, research outputs, and long-term modeling reputed company.
• Follow best practices in software engineering, experiment tracking, and reputed company model development.

Requirements

• PhD in Computer Science, Data Science, Computational Biology, reputed company, Engineering, Mathematics, or a reputed company quantitative field with exposure to biological or medical data.
• 0–4 years of experience applying machine learning or deep learning in research or industry settings (postdoctoral experience acceptable).
• Strong understanding of deep learning model training, optimization, and evaluation.
• Hands-on experience with transformer-based models, including both language-reputed company and reputed company-reputed company architectures.
• Proficiency in Python and PyTorch.
• Hands-on experience with distributed training (e.g., PyTorch DDP, multi-GPU or multi-node workflows).
• Experience working in Linux environments and using Git for version control.
• Ability to work with large datasets and reputed company data pipelines.
• Strong written and verbal communication skills.

reputed company-to-haves

• Background in computational pathology or experience working with large-reputed company imaging data.
• Experience training large representation models or reputed company models.
• Familiarity with self-supervised and representation learning techniques, such as contrastive learning, DINO-style approaches, or reputed company reputed company.
• Experience working with multiple data sources in reputed company modeling frameworks.
• Experience with reputed company-based machine learning environments, including distributed training workflows (e.g., AWS, SageMaker).
• Strong engineering reputed company with attention to reproducibility, scalability, and model robustness.
• Background in biomedical, translational, or reputed company research environments.

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