Senior Data Engineer, AI Infrastructure

November 25

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Arbiter

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SaaS • Healthcare Insurance

Arbiter is a care orchestration platform that unifies patients, payers, and providers on a single intelligent system to automate referrals, prior authorizations, scheduling, and care optimization. The modular SaaS embeds into existing clinical workflows, consolidates clinical, financial, and policy data, and uses AI-driven routing and automation to improve referral matching, reduce administrative burden, and ensure timely, cost-effective care. Arbiter is built by industry operators and integrates with EMRs and payer systems to deliver closed-loop execution across the care continuum.

11 - 50 employees

Founded 2025

☁️ SaaS

⚕️ Healthcare Insurance

📋 Description

• AI/ML Pipeline Development: 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. • MLOps & Infrastructure: 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). • Embedding & Vector Systems: 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. • AI Platform & Tooling: 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. • Cross-Functional Collaboration: 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. • Mentorship & Growth: Actively participate in mentoring junior engineers, contributing to our team's growth through technical guidance, code reviews, and knowledge sharing. • Hiring & Onboarding: Play an active role in interviewing and onboarding new team members, helping to build a world-class data engineering organization.

🎯 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. • Intimate knowledge of and ability to implement unit, integration, and functional testing strategies. • Experience providing technical leadership and guidance, and thinking strategically and analytically to solve problems. • Friendly communication skills and ability to work well in a diverse team setting. • Demonstrated experience working with many cross-functional partners.

🏖️ Benefits

• Highly Competitive Salary & Equity Package: Designed to rival top FAANG compensation, including meaningful equity. • Generous Paid Time Off (PTO): To ensure a healthy work-life balance. • Comprehensive Health, Vision, and Dental Insurance: Robust coverage for you and your family. • Life and Disability Insurance: Providing financial security. • Simple IRA Matching: To support your long-term financial goals. • Professional Development Budget: Support for conferences, courses, and certifications to fuel your continuous learning. • Wellness Programs: Initiatives to support your physical and mental health.