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Arbiter · 2 months ago

Senior Data Engineer, AI Infrastructure

United States

Full-time

Remote

Senior Level, Lead/Staff

$180K/yr - $240K/yr

8+ years exp

Arbiter is the AI-powered care orchestration system that unites healthcare. The Senior Data Engineer, AI Infrastructure will be responsible for building and maintaining the platform that powers the company's intelligent operating system, focusing on creating robust data pipelines and infrastructure for AI and machine learning systems.

Artificial Intelligence (AI)Health CareMedical

Responsibilities

As a Senior Data Engineer, AI Infrastructure, you are the architect of our AI/ML systems

You will build and maintain the platform that powers our intelligent operating system, creating the robust pipelines and infrastructure for data processing, model training, and inference

Your work is the foundation that enables our AI engineering teams to build, deploy, and scale their products effectively

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 PlatformsContainerizationML OrchestrationData PipelinesCommunication SkillsTechnical LeadershipCross-Functional CollaborationMentorship

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