Data Engineer

Responsibilities

• Analyze the performance and impact of data used throughout the model training lifecycle
• Investigate anomalous model behavior and rigorously identify the data issues that drive poor downstream performance
• Design, build, and improve the data cleaning, transformation, and quality-control steps required to produce high-quality training data
• Research, evaluate, and develop frontier methods for improving data quality and effectiveness in AI model development
• Apply statistical techniques and empirical analysis to make informed, data-driven decisions about dataset quality and model outcomes
• Partner across teams to identify where data needs exist and define the highest-impact opportunities for new data acquisition and improvement
• Build and maintain production-grade data pipelines, tooling, and software systems that ingest, process, validate, and deliver data for training
• Develop metrics, evaluation frameworks, and monitoring systems to assess how data quality influences model behavior at scale
• Fuse data from multiple sources into reliable, usable datasets for research and production model training
• Create shared datasets, tooling, and internal data products that enable other teams to analyze, debug, and improve model performance

Basic Qualifications

• Bachelor’s degree in computer science, data science, physics, mathematics, or a STEM discipline
• 1+ years of data/software engineering experience (internship experience is applicable)
• Experience in implementing or analyzing language models or neural networks

Preferred Skills and Experience

• Professional experience in analytics, data science, machine learning, or data engineering
• Experience building and operating production data pipelines for neural network or large-scale machine learning workloads
• Strong experience with Python and the broader ecosystem of libraries and tools used in modern machine learning and data development
• Experience working with Parquet or similar columnar storage formats in large-scale data systems
• Familiarity with Kubernetes and distributed production environments
• Experience developing predictive models and machine learning pipelines, including clustering, forecasting, anomaly detection, or related techniques
• Experience working with very large-scale datasets, including terabyte- to petabyte-scale data systems
• Strong statistical intuition and the ability to use quantitative analysis to guide technical and product decision, including familiarity of scaling ladder design studies
• Ability to operate effectively in a dynamic environment with evolving priorities, changing requirements, and fast-moving technical challenges
• Demonstrated ability to take ownership of ambiguous problems, drive projects independently, and develop new expertise where needed

Compensation and Benefits

• $240,000 - $280,000 USD base salary
• Equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks