Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 3
Data Engineering @ 6
Data Pipelines
Data Science @ 3
Machine Learning
Mathematics @ 3
Statistics @ 6
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge.
As a Data Engineer / AI Engineer on SpaceXAI's Data team, you will develop the systems, processes, and production code that power data acquisition, preparation, quality evaluation, and delivery for model training.
You will work closely with acquisition teams, ML engineers, and software engineers to identify data needs, build scalable data pipelines, and continuously improve the quality of the data that shapes model behavior. The ideal candidate combines strong software engineering fundamentals and excellent coding practices with deep intuition for statistics, neural networks, and how data quality influences training outcomes.
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
Requirements
- 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
Benefits
Base salary is just one part of our total rewards package at SpaceXAI, which also includes 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.