Foundational Engineer (Junior) — Multimodal & Medical AI Systems

About the Role

You will join the engineering team building the infrastructure and tools behind our 3D multimodal foundation modeland agentic medical AI systems.
As a junior foundational engineer, you’ll work closely with senior engineers and AI researchers to implement data pipelines, training tools, evaluation workflows, and model-serving components that power cutting-edge clinical AI.

This is an ideal role for engineers early in their career who want to learn ML systems, multimodal AI, and high-performance engineering—while contributing to real, impactful healthcare products.

 

What You Will Work On

Model Training & Development Support

• Build and maintain components of the training pipeline for models that combine3D imaging and clinical text.
• Implement data loaders, preprocessing utilities, and training scripts in Python/PyTorch.
• Help run training experiments, monitor performance, and assist with debugging model or data issues.

Data & Multimodal Engineering

• Work with large-scale imaging and text datasets, supporting ingestion, cleaning, and preparing data for model training.
• Write tools to process3D medical volumes (DICOM, NIfTI) and associated metadata.
• Collaborate with the data team to ensure efficient, reliable dataset access.

Infrastructure & Tooling

• Contribute to internal tooling for experiment tracking, evaluation dashboards, and reproducibility workflows.
• Assist in improving training performance through code optimizations, batching strategies, and caching techniques.
• Support deployment of models and agents into testing environments.

Cross-Functional Collaboration

• Work directly with senior engineers, AI researchers, and product teams to refine requirements and deliver engineering features.
• Participate in design discussions, brainstorms, and sprint planning.
• Learn to translate research ideas into engineering tasks and production-ready components.

 

Why This Role Is Great for Junior Engineers

• You get hands-on exposure to cutting-edgemultimodal AI, including 3D imaging, LLMs, and clinical applications.
• You will be mentored by experienced ML systems and research engineers.
• You will contribute meaningful engineering work to models used in real healthcare settings.
• You’ll rapidly build expertise in areas that typically require years of specialization:
  • Distributed training
  • Multimodal data pipelines
  • Model serving and evaluation
  • Healthcare-specific AI infrastructure

 

What We’re Looking For

• 1–4 years experience in software engineering, ML engineering, data engineering, or related fields.
• Solid programming skills in Python; familiarity with PyTorch or another deep learning framework is a plus.
• Basic understanding of machine learning concepts and motivation to learn more.
• Comfort working with data-intensive systems and debugging technical issues.
• Good communication skills and ability to work collaboratively in a cross-disciplinary team.
• Curiosity, willingness to experiment, and enthusiasm for clinical and AI innovation.

 

Nice to Have (But Not Required)

• Exposure to ML training pipelines, GPU programming, or distributed computing.
• Familiarity with medical imaging (DICOM, NIfTI) or 3D data processing.
• Experience with Docker, Linux, or cloud infrastructure.
• Contributions to open-source ML projects or personal ML projects.
• Interest in healthcare, radiology, cardiology, or clinical workflows.

 

What We Offer

• Strong mentorship from senior engineers and researchers.
• Training opportunities in ML systems, multimodal modeling, and medical AI.
• Competitive compensation and growth path into senior foundational engineering roles.
• Access to advanced compute (A100/H100 clusters) and real clinical datasets.
• Mission-driven work with clear social and clinical impact.