About Arbiter

Engineering Culture & Values

• Engineering Excellence: Taking immense pride in our technical craft and the products we build, treating both with utmost respect and care.
• Impact-Driven Development: Firmly committed to engineering high-quality, fault-tolerant, and highly scalable systems that evolve seamlessly with business needs, minimizing disruption.
• Collaboration Over Ego: Valuing exceptional work and groundbreaking ideas above all else. We seek talented individuals who are accustomed to working in a fast-paced environment and are driven to ship often to achieve significant impact.
• Continuous Growth: Fostering an environment of continuous learning, mentorship, and professional development, where you can deepen your expertise and grow your career.

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.

Minimum Qualifications

• 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 Qualifications

• 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.

Additional Details

Job 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.

Salary Range