AI Engineer

Quantivly’s mission is to increase access to medical imaging and enhance imaging care. Spun out of Boston Children’s Hospital, we are building the “air traffic control” system for medical imaging providers, using computers for what they do best (solving complex orchestration problems) so clinicians and staff can focus on what matters most: patient care.

Quantivly has built the first-of-its-kind unified data layer that unlocks imaging operational data previously trapped within hospital systems. Our initial application gives imaging providers unprecedented visibility into their operations. We have built a novel foundation model of operations and are now rapidly expanding to create the automation layer that will redefine how radiology departments operate.
We are hiring a new AI Engineer to accelerate this vision and turn cutting-edge operational data into real-world impact for radiology teams.

As an AI engineer at Quantivly, you will be a core member of the AI team and help build the intelligent systems that power our automation layer and knowledge-graph-driven insights. You will create end-to-end AI solutions that fuse diverse data signals to power actionable agents.

Your day-to-day responsibilities will include (but are not limited to):

• Building reliable data pipelines and training workflows.
• Designing, implementing, testing, and deploying AI models and agents aligned with the product roadmap.
• Integrating models and agents to our platform services to support real workflow automation.
• Bridging the gap between prototype and production by working cross-functionally to ship AI-powered features.
• Ensuring model quality through evaluation frameworks, testing, and reproducibility.
• Improving CI/CD for ML, model deployment systems, monitoring, cloud-based training, and scalable inference.
• Expanding and optimizing our AI repositories and model library to accelerate the creation and deployment of new models and agents.
• Staying current with applied ML and bringing practical advancements into our stack.

We are transforming radiology by injecting AI into imaging operations.

If you want to build real-world AI end-to-end –from training to deployment to impact– join Quantivly!

Salary: $160k-180k, Equity: 0-0.25%

• Develop and maintain end-to-end machine learning pipelines, from data ingestion to production integration, supporting core components of our automation layer.
• Implement clean, scalable Python code and reusable ML components that integrate seamlessly with our platform and MLOps tooling.
• Prepare, process, and validate diverse data sources (HL7, DICOM metadata, operational logs, radiology report text) to ensure high-quality model inputs and consistent labeling.
• Design evaluation frameworks, run experiments, and clearly communicate results, tradeoffs, and model behavior to both technical and non-technical stakeholders.
• Stay updated with the latest advancements in machine learning and artificial intelligence, integrating relevant insights into ongoing projects.
• Contribute to internal documentation, best practices, and shared AI standards to support a growing AI team and an expanding automation ecosystem.

• 3+ years of professional ML engineering experience
  , with demonstrated end-to-end ownership of model-driven features.
• Strong applied ML skills: model development, feature engineering, evaluation, and productionization.
• Experience with modern LLMs, embeddings, or retrieval-based methods (even if not in production).
• Solid Python engineering competency (beyond notebooks): testing, structure, reliability.
• Experience with core ML tooling (e.g., PyTorch or Tensor Flow, Hugging Face, scikit-learn).
• Strong problem-solving, debugging, and analytical skills.
• Excellent communication and ability to collaborate in a fast-paced environment.
• High ownership mindset and comfort working in an early-stage team where responsibilities evolve.
• Bachelor’s degree or greater in Computer Science (preferred), Information Technology, or equivalent work experience.

• Experience with Docker, or cloud services for ML deployment.
• Experience with Reinforcement Learning.
• Experience…