Data Engineer (Core Team)

The explosive growth of artificial intelligence and machine learning models has created an unprecedented demand for high-integrity, impeccably structured data. While algorithms and transformer architectures capture public headlines, the true bottleneck in artificial intelligence development lies in data engineering: building the robust pipelines, cleaning messy unstructured datasets, and preparing reliable inputs that allow next-generation AI agents to reason effectively.

At the center of this technological wave is micro1, the leading AI data lab powering frontier model training and agent evaluations. To support its rapid expansion, micro1 is actively hiring for a full-time, remote Data Engineer position. Offering a competitive base salary range of $100,000 to $150,000 USD, equity compensation, performance bonuses, and comprehensive benefits including up to 100% health insurance premium reimbursement, this role represents an elite career opportunity for data professionals looking to shape the future of artificial intelligence.

Whether you are an experienced data pipeline architect looking to pivot into artificial intelligence infrastructure or a backend engineer eager to work with cutting-edge LLMs, this comprehensive guide covers everything you need to know about the role, required technical stacks, benefits, and a step-by-step roadmap to complete your application.

The Core Mission: Powering Frontier AI Through Scalable Data Architecture

Training advanced artificial intelligence models requires massive quantities of curated, pristine data. Traditional data engineering focuses on business intelligence and transactional reporting. In contrast, data engineering at an AI lab like micro1 involves building pipelines that feed machine learning training loops, fine-tuning datasets, and evaluation harnesses.

As a Data Engineer on micro1’s core team, you will bridge the gap between raw, unstructured digital information and the rigorous datasets required by frontier AI researchers. You will handle complex ingestion tasks, automate anomaly detection, and ensure data integrity across multi-modal storage systems.

Key Responsibilities and Scope of Work:

• ETL Pipeline Development: Design, develop, maintain, and scale robust ETL pipelines capable of processing complex structured and unstructured datasets efficiently.
• Data Transformation & Cleaning: Collect, clean, transform, and organize raw inputs into pristine, reliable formats suitable for machine learning consumption.
• Exploratory Data Analysis: Conduct deep exploratory data analysis (EDA) to uncover trends, systemic anomalies, data-quality issues, and distribution skews.
• Cross-Functional Collaboration: Partner closely with researchers, data scientists, and core engineering teams to prepare specialized datasets for upcoming AI and LLM initiatives.
• Database & Schema Management: Develop and maintain scalable data models, database schemas, and optimized storage solutions using modern relational databases like PostgreSQL and MySQL.
• Query Optimization: Write, profile, and optimize complex SQL queries to accelerate data extraction, transformation, and analytical reporting.
• Automation & Reliability: Automate data validation, error handling, reporting, and recurring data processing workflows while documenting technical decisions.

Required Skills and Technical Qualifications

Because micro1 builds the human and technical intelligence layer for high-performing AI systems, technical expectations are exceptionally high. Successful candidates possess a robust blend of software engineering fundamentals and data-handling mastery:

• Core Programming Proficiency: Strong, demonstrable proficiency in Python and advanced SQL.
• Pipeline Architecture: Hands-on, professional experience designing, building, and maintaining production-grade ETL pipelines.
• Data Processing Libraries: Mastery of essential Python data manipulation libraries, specifically Pandas and NumPy.
• Database Expertise: Proven experience working with relational database systems, with particular emphasis on PostgreSQL and MySQL.
• Data Modeling: Solid foundational understanding of data modeling principles, normalization, schema design, and data transformation strategies.
• Development Environments: Familiarity with modern developer environments such as Jupyter Notebook, VS Code, or PyCharm.

Preferred "Nice-to-Have" Experience:

Candidates who bring exposure to artificial intelligence and machine learning workflows will stand out during evaluation. Beneficial secondary experiences include working with scikit-learn, integrating Hugging Face Transformers, utilizing the OpenAI API, or managing specialized datasets specifically tailored for LLM fine-tuning.

Comprehensive Compensation, Benefits, and Remote Culture

micro1 is committed to building a world-class, remote-first workforce by offering competitive financial packages that rival top Silicon Valley tech firms:

• Competitive Base Salary: A base compensation range of $100,000 to $150,000 USD per year.
• Equity Compensation: Every full-time employee is eligible for equity participation, aligning your long-term success with the explosive growth of the company.
• Performance Bonuses: Additional earning potential through performance-based bonuses tied to company milestones and role impact.
• Robust Healthcare Benefits: Comprehensive medical coverage including up to 100% reimbursement for health insurance premiums.
• Retirement & Paid Time Off: Generous paid time off (PTO) policies and a 401(k) retirement plan featuring a competitive company match.

Step-by-Step Guide to Completing Your Application Successfully

Securing a full-time engineering role at an elite AI lab requires precision and attention to detail. Follow these steps to ensure your application makes an immediate impact:

1. Tailor Your Resume to Data Engineering & Python: Highlight your production ETL experience, database management skills, and proficiency with Pandas and NumPy right at the top of your resume.
2. Showcase AI/ML Familiarity: If you have previously prepared datasets for machine learning models, fine-tuned open-source models, or built pipelines that integrate LLM APIs, feature those projects prominently.
3. Complete All Application Fields Promptly: Navigate to the application portal, fill out all required information accurately, and complete any preliminary technical screening assessments without delay.

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