Arbiter is the AI-powered care orchestration system that unites healthcare. Today, healthcare runs on $100B+ in fragmented point solutions that can't see the full picture. We replace them with a single intelligent system that sits on top of EMRs and existing workflows, unifies clinical, policy, and financial data, then automates the actions that close care gaps - starting with site-of-care optimization.
Requirements
- 8+ years of deep, hands-on experience in Data Engineering, MLOps, or AI/ML Infrastructure, ideally within a high-growth tech environment.
- Exceptional expertise in data structures, algorithms, and distributed systems.
- Mastery in Python for large-scale data processing and ML applications.
- Extensive experience designing, building, and optimizing complex, fault-tolerant data pipelines specifically for ML models (e.g., feature engineering, training data generation).
- Profound understanding and hands-on experience with cloud-native data and AI platforms, especially Google Cloud Platform (GCP) (e.g., Vertex AI, BigQuery, Dataflow, GKE).
- Strong experience with containerization (Docker) and orchestration (Kubernetes) for deploying and scaling applications.
- Demonstrated experience with modern ML orchestration (e.g., Kubeflow, Airflow), data transformation (dbt), and MLOps principles.
Responsibilities
- Design, develop, and maintain robust, scalable data pipelines specifically for our AI models. This includes data ingestion, cleaning, transformation, classification, and tagging to create high-quality, reliable training and evaluation datasets.
- Build and manage the AI infrastructure to support the full machine learning lifecycle. This includes automating model training, versioning, deployment, and monitoring (CI/CD for ML).
- Architect and operate scalable systems for generating, storing, and serving embeddings. Implement and manage vector databases to power retrieval-augmented generation (RAG) and semantic search for our AI agents.
- Champion and build core tooling, frameworks, and standards for the AI/ML platform. Develop systems that enable AI engineers to iterate quickly and self-serve for model development and deployment.
- Partner closely with AI engineers, product managers, and software engineers to understand their needs. Translate complex model requirements into stable, scalable infrastructure and data solutions.
- Actively participate in mentoring junior engineers, contributing to our team's growth through technical guidance, code reviews, and knowledge sharing.
- Play an active role in interviewing and onboarding new team members, helping to build a world-class data engineering organization.
Other
- This role can be remote or onsite, based in our New York City or Boca Raton offices, in a fast-paced, collaborative environment where great ideas move quickly from whiteboard to production.
- Friendly communication skills and ability to work well in a diverse team setting.
- Demonstrated experience working with many cross-functional partners.
- Experience providing technical leadership and guidance, and thinking strategically and analytically to solve problems.
