SpaceXAI is building AI systems that push the frontier of human knowledge and scientific discovery. This role focuses on ensuring high-quality data throughout the model training lifecycle, partnering with acquisition teams, and building production pipelines for data acquisition, preparation, quality evaluation, and delivery.
Data Engineer at SpaceXAI
On-site - Palo Alto, CA
More jobs at SpaceXAISalary
USD 150,000 - 210,000
Requirements
Skills
- Bachelor’s degree in computer science, data science, physics, mathematics, or a STEM discipline
- 1+ years of data/software engineering experience (internship applicable)
- Experience in implementing or analyzing language models or neural networks
- 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
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
Technologies
PythonParquetKubernetesdistributed production environmentslarge-scale data systemsneural networksmachine learning pipelinesclusteringforecastinganomaly detection
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