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 @ 2
Communication @ 6
Docker
Machine Learning @ 2
Prioritization @ 6
Reinforcement Learning
Spark @ 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 is seeking a hands-on technical team member to help develop AI systems and advance large-scale model training. The role focuses on scaling synthetic coding data, distilling intelligence into flash models, optimizing mid-training data mixtures, engineering long-context data recipes, and evaluating mid-training checkpoints.
Responsibilities
- Scale synthetic coding data to trillions of tokens using large-scale Docker verification.
- Distill the intelligence of flagship models into flash models through synthetic data generation.
- Optimize mid-training data mixtures to increase the ceiling for reinforcement learning.
- Engineer long-context data recipes.
- Develop robust and diverse evaluations for mid-training checkpoints.
Requirements
- Expertise in machine learning and large-model scaling, with familiarity across various scaling laws.
- Strong ability to design machine-learning experiments.
- Familiarity with state-of-the-art techniques for curating AI training data across text, image, audio, and video modalities.
- Strong engineering abilities with Spark, Ray, and other frameworks for large-scale data processing.
- Strong communication, prioritization, and knowledge-sharing skills.
Compensation and Benefits
- Base salary of $180,000–$440,000 USD per year.
- Equity.
- Comprehensive medical, vision, and dental coverage.
- Access to a 401(k) retirement plan.
- Short- and long-term disability insurance.
- Life insurance and various other discounts and perks.
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