Head of Engineering

Head Of Engineering

We're seeking a Head of Engineering to lead our system development, manage our engineering team, and streamline our operations.

Core Responsibilities

Lead and mentor a team of ML/AI engineers in developing and deploying production-grade medical imaging solutions. Orchestrate the end-to-end ML lifecycle, from model development to production deployment using Nvidia Triton Servers. Design and implement scalable MLOps infrastructure to support model training, validation, and monitoring. Establish and maintain CI/CD pipelines for reliable model deployment and updates. Collaborate with cross-functional teams to integrate AI solutions into our medical device products. Drive technical decisions while ensuring compliance with FDA regulations and medical device standards. Partner with product and clinical teams to translate cardiologist needs into technical requirements.

Technical Leadership

Architect and oversee development of RESTful APIs for model serving and integration. Lead infrastructure decisions for cloud-based deployment using GKE and Terraform. Implement monitoring and observability solutions using Sentry for production model performance. Guide the team in building visualization tools for model outputs, as well as evaluations measuring the efficacy of existing models on new datasets. Establish best practices for version control, code review, and documentation.

Required Experience

Strong background in Python, with experience in Django and REST frameworks. Proven track record of leading technical teams and managing complex projects. Experience with containerization (Docker) and orchestration (Kubernetes). Understanding of ML model serving architectures and MLOps best practices.

Preferred Qualifications

Experience with medical imaging or healthcare applications. Familiarity with FDA requirements for medical devices. Background in React and modern frontend development. Experience with ClearML or similar ML experiment tracking platforms. Knowledge of cloud infrastructure management using Terraform.

Tech Stack Proficiency

Infrastructure: GKE, Helm, Terraform, ECR Backend: Django, Python, Nvidia Triton Servers Frontend: React EMLOps: ClearML, GitHub Actions Monitoring: Sentry

What Success Looks Like

Within 3 months: Fully understand our ML pipeline and deployment architecture. Within 6 months: Lead significant improvements to our model deployment process. Within 1 year: Successfully shepherd new AI features through FDA approval process.

Location

Los Angeles, preferably in person, remote can also be acceptable.