SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization values individuals who enjoy challenging themselves and thrive on curiosity. With a flat organizational structure, all employees are expected to be hands‑on, contribute directly to the mission, and demonstrate strong communication, work ethic, and prioritization skills. As an ML Infrastructure Engineer, you will play a pivotal role in building and optimizing the reliable, high‑performance ML platform that powers recommendations on X, and will work closely with ML teams to deliver impactful solutions.
ML Infrastructure Engineer en xAI
Presencial - Palo Alto, California, United States
Más vacantes en xAISalary
USD 180,000 - 440,000
Requirements
Skills
- Bachelor, Master, Post-graduate or PhD in computer science, machine learning, or other quantitative discipline; or equivalent work experience
- 2+ years of industry experience working with high traffic or large-scale production environments, distributed systems, GPU infrastructure, and/or deep learning applications
- 2+ years experience with ML platforms, training infrastructure, or close collaboration with modeling engineers and data scientists
- Strong proficiency with Python and experience with compiled languages such as C++ or Rust
- Deep familiarity with modern ML frameworks such as JAX or PyTorch
- Low-level understanding of compute systems, including distributed storage, NVIDIA drivers, CUDA toolkits, and networking
- Comfortable with Linux systems and orchestration tools
- Experience with job schedulers (e.g., Slurm), configuration management (Puppet/Ansible), or related infrastructure tooling
Responsibilities
- Designing, building, and scaling GPU compute infrastructure, training frameworks, and experimentation tools to enable rapid iteration on ML hypotheses
- Developing data pipelines and integrating large-scale data, training, and inference systems
- Collaborating with ML teams to productionize models and ensure seamless integration across the stack
- Ensuring scalability, reliability, and efficiency of large-scale machine learning systems
- Working across the full stack to solve complex problems independently
- Mentoring junior engineers and contributing to the growth of the team
Technologies
PythonC++RustJAXPyTorchLinuxSlurmPuppetAnsibleNVIDIA driversCUDA
Descubre si tu currículum está listo para esta vacante
Mira cómo nuestra IA puede optimizar tu currículum y aumentar tus chances en este puesto.