Founding Forward-Deployed ML Engineer

A Founding Forward-Deployed ML Engineer will work closely with hospital partners to evaluate and deploy medical imaging AI in real-world clinical settings, bridging benchmark performance and clinical reliability to drive real-world healthcare impact. This role sits at the intersection of research, product deployment, and clinical operations, helping Tessel AI translate models into trusted patient care.

What youll do

• Build reproducible evaluation pipelines and validation workflows for medical imaging AI in clinical settings.
• Lead forward-deployed deployments by working on-site with hospital partners to integrate models into workflows.
• Analyze model generalization, failure modes, and uncertainty to inform clinical reliability assessments.
• Integrate ML models into clinical imaging systems and radiology pipelines (DICOM/PACS).
• Translate clinical needs into technical requirements and drive cross-functional projects to completion.
• Support regulatory submissions and clinical evaluations (FDA pathways) and maintain related documentation.
• Ensure data privacy and regulatory compliance (HIPAA) in ML deployments.
• Establish and maintain MLops practices for deployment, monitoring, and evaluation (Python, PyTorch/TensorFlow, Docker, Kubernetes).

What Tessel AI: Rigorous Medical Imaging AI Evaluation for Hospitals is looking for

• Willingness to travel to hospital sites and work on-site for customer deployments as needed.
• Practical MLOps and model evaluation skills: building reproducible evaluation pipelines, model validation/monitoring, and familiarity with Python and common ML frameworks and tooling (e.g., PyTorch/TensorFlow, Docker, Kubernetes).
• 2+ years of Machine Learning/Engineering experience.
• Strong CS fundamentals; degree in Computer Science, Engineering, Math, or a related field (or equivalent practical experience).
• Hands-on experience deploying ML models to cloud platforms (AWS/GCP/Azure) with containerized environments and ML-focused CI/CD pipelines.
• Experience navigating healthcare data privacy and regulatory compliance (e.g., HIPAA, FDA considerations) in ML deployments.
• Experience supporting regulatory submissions and clinical evaluation processes (e.g., preparing evidence for FDA 510(k) or De Novo pathways).
• Strong customer-facing skills and ability to own engagements: communicate with clinical and industry partners, translate clinical needs into technical solutions, and drive cross-functional projects to completion.
• Hands-on expertise in medical imaging workflows and integration (DICOM/PACS, radiology pipelines) and integrating ML models into clinical systems.