Data Engineer

The explosive growth of artificial intelligence and machine learning models relies on a critical, often understated foundation: robust, ultra-scalable data infrastructure. As frontier labs push the boundaries of reasoning, agent automation, and multimodal learning, the volume and complexity of data pipelines required to ingest, process, and structure information have scaled exponentially. Building systems that can support real-time experimentation and model evaluation at global scale requires elite engineering talent.

micro1—the leading AI data lab powering frontier model training and intelligent agent evaluations—is expanding its core team with a high-impact, full-time remote opening: the Data Engineer position. Offering a competitive base salary range of $100,000 to $150,000 USD, alongside comprehensive equity compensation, performance bonuses, health insurance reimbursement, and a 401(k) match, this role places you at the epicenter of modern AI architecture.

Whether you are a seasoned data infrastructure expert looking to scale distributed pipelines, optimize cloud-native data systems, or shape the foundational data layer for next-generation AI workflows, this comprehensive guide covers the technical scope, required skills, compensation breakdown, and a step-by-step pathway to complete your application.

The Core Mission: Engineering the Human Intelligence Layer

Traditional data engineering focuses on business intelligence, transactional reporting, and standard data warehousing. However, engineering infrastructure for artificial intelligence labs presents a distinct, highly complex set of challenges. micro1 transforms real-world expert knowledge into high-quality training data, evaluations, and feedback loops that govern how artificial intelligence systems learn and reason.

As a Data Engineer on micro1’s core team, you will not just maintain existing scripts; you will architect the distributed systems that process massive ingestion streams, validate complex datasets, and ensure absolute data integrity across multiple cloud storage layers. Your work directly enables AI researchers, data scientists, and cross-functional engineering units to iterate rapidly on experimental models.

Key Responsibilities and Scope of Work:

• Scalable Pipeline Architecture: Design, build, and maintain robust, scalable data pipelines to ingest, process, and transform high-volume datasets from diverse sources.
• Distributed Processing Optimization: Develop and optimize distributed data processing workflows using Apache Spark and modern cloud-native technologies.
• Hybrid Storage Management: Build and maintain high-performance data storage solutions across both relational (SQL) and non-relational (NoSQL) database systems, ensuring reliability and fault tolerance.
• AWS Cloud Integration: Design and implement secure, scalable data architectures on Amazon Web Services (AWS) to support high-throughput data distribution and processing.
• Code Efficiency & Validation: Write clean, highly efficient Python and SQL code to extract, transform, validate, and analyze massive data structures.
• Cross-Functional Collaboration: Partner closely with AI researchers and machine learning engineers to support data-intensive experimentation, prompt engineering workflows, and agent evaluation systems.
• Automation & Monitoring: Implement end-to-end automation, orchestration, and monitoring frameworks to guarantee operational reliability across all data layers.

Preferred Qualifications and Technical Competencies

Because micro1 operates at the cutting edge of artificial intelligence research, candidates must demonstrate elite technical execution, architectural vision, and hands-on proficiency with modern data stacks:

• Programming Mastery: Strong, demonstrable proficiency in Python and SQL, with clean coding practices and experience building production-grade data services.
• Distributed Frameworks: Hands-on experience with distributed data processing frameworks such as Apache Spark.
• Cloud-Native Architecture: Proven expertise working with AWS data services and cloud-native infrastructure design.
• Database Versatility: Deep working knowledge of both SQL and NoSQL database management, optimization, and partitioning strategies.
• Large-Scale Data Operations: Experience successfully managing, cleaning, and processing massive datasets in distributed environments under strict performance constraints.
• Bonus Qualifications: Prior exposure to AI/ML research environments, data visualization libraries (Matplotlib, Seaborn, Plotly), or LLM-specific data workflows (training corpora, evaluation sets, or prompt testing pipelines).

Navigating the micro1 Recruitment Process

micro1 utilizes an advanced, AI-assisted recruitment platform designed to streamline candidate screening while maintaining rigorous human evaluation standards. To ensure your application receives priority review, follow these strategic steps:

1. Tailor Your Resume to Data Engineering at Scale: Explicitly highlight your experience with Python, SQL, Apache Spark, and AWS cloud environments in your CV and portfolio.
2. Showcase Distributed Systems Experience: Detail specific projects where you optimized data partitioning, reduced query latency, or scaled pipelines to handle millions of records.
3. Complete the Application Promptly: Submit your comprehensive application through the official portal to ensure seamless tracking and evaluation by the hiring team.

Take the next major step in your engineering career by building the infrastructure that powers the future of artificial intelligence.