Mid-Training ML Engineer — Scale Data & RL Models

SpaceX Super Intelligence; Palo Alto (CA); On-site; USD 180,000 - 440,000; Full time

Benefits offered by this job

Equity

Comprehensive medical, vision & dental

401(k) retirement plan

Disability & Life insurance

Job summary

SpaceXSI in Palo Alto, CA is seeking a machine learning specialist to scale large models and build synthetic data pipelines. You will contribute across the ML stack, from data curation to evaluation, collaborating with teams developing Grok, Colossus and related tools.

You will design experiments, optimize mid-training data mixtures, and help convert flagship models into robust, production-ready systems. Competitive compensation and comprehensive benefits provided.

Qualifications

• Expertise in ML and large model scaling, with familiarity across scaling laws.
• Strong ability to design ML experiments.
• Familiarity with techniques for curating AI training data for text, image, audio, and video modalities.
• Strong engineering abilities in Spark, Ray, and other frameworks for large-scale data processing.

Responsibilities

• Scale synthetic coding data to trillions of tokens with large-scale docker verification.
• Distill the intelligence of flagship models into flash models through synthetic data generation.
• Optimize mid-training data mixtures to boost the ceiling for RL.
• Engineer long-context data recipes.
• Develop robust and diverse evaluation for mid-training checkpoints.

Skills

ML scaling

Experiment design

Model data curation

Large-scale data processing

Tools

Spark

Ray

Job description

SpaceXSI in Palo Alto, CA is seeking a machine learning specialist to scale large models and build synthetic data pipelines. You will contribute across the ML stack, from data curation to evaluation, collaborating with teams developing Grok, Colossus and related tools.

You will design experiments, optimize mid-training data mixtures, and help convert flagship models into robust, production-ready systems. Competitive compensation and comprehensive benefits provided.