Remote-Friendly AI Data Infrastructure Engineer

Haleos, Inc.; Austin (TX); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

401(k) with company match

Flexible PTO policy

Remote-friendly work environment

Job summary

Haleos, Inc. is seeking an AI Research Engineer in Austin, TX (Hybrid) to architect and implement the data engine powering our AI systems.

You will design scalable pipelines and robust ETL processes across multiple data streams, enabling advanced insights with enterprise-grade performance and privacy. You will work with ML engineers to align data with model requirements, implement privacy-preserving architectures, and optimize retrieval at scale using vector databases.

Qualifications

• Strong experience designing and implementing production-grade data pipelines.
• Proficiency with vector databases and embedding-based retrieval systems.
• Experience with real-time data synchronization across integrations.
• Solid knowledge of database design, indexing, and query optimization.

Responsibilities

• Design and implement scalable data pipelines that process millions of data points.
• Build intelligent systems for real-time data synchronization across CRM, finance, and dev tools.
• Architect vector database solutions optimized for semantic search and contextual retrieval at scale.
• Develop ETL systems transforming heterogeneous data into structured formats.
• Create automated data validation and quality assurance for training data.
• Build monitoring and observability tools for data pipelines.
• Collaborate with ML engineers to ensure data meets model requirements.
• Implement privacy-preserving data isolation architectures.

Skills

Data pipelines

ETL systems

Python

SQL

Vector databases

Real-time data

APIs & webhooks

TypeScript

Tools

Pinecone

Weaviate

Airflow

dbt

Job description

Haleos, Inc. is seeking an AI Research Engineer in Austin, TX (Hybrid) to architect and implement the data engine powering our AI systems.

You will design scalable pipelines and robust ETL processes across multiple data streams, enabling advanced insights with enterprise-grade performance and privacy. You will work with ML engineers to align data with model requirements, implement privacy-preserving architectures, and optimize retrieval at scale using vector databases.