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Arbiter · 1 month ago

Senior Data Engineer, AI Infrastructure

United States

Full-time

Remote

Senior Level, Lead/Staff

$180K/yr - $240K/yr

8+ years exp

Arbiter is an AI-powered care orchestration system uniting healthcare by replacing fragmented point solutions with a comprehensive intelligent system. As a Senior Data Engineer for AI Infrastructure, you will architect and maintain the platform that powers the company's AI/ML systems, creating robust pipelines and infrastructure for data processing and model training.

Artificial Intelligence (AI)Health CareMedical

Responsibilities

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

Qualification

Data EngineeringMLOpsPythonCloud PlatformsData PipelinesContainerizationML OrchestrationVector DatabasesDeep Learning FrameworksCommunication SkillsTechnical Leadership

Required

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

Preferred

Experience with vector databases (e.g., Pinecone, Elasticsearch) and building embedding generation pipelines

Experience with MLOps platforms and tools (e.g., MLflow, Weights & Biases) for experiment tracking and model management

Experience with advanced data extraction and correlation techniques, especially from unstructured medical data sources (e.g., PDF charts, clinical notes)

Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)

Familiarity with data governance, data security, and compliance frameworks (e.g., HIPAA, GDPR) in a highly regulated industry