GPU-Powered ML Platform Engineer

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

Benefits offered by this job

Equity

Medical insurance

Vision insurance

Dental coverage

401(k)

Disability insurance

Life insurance

Employee discounts

Job summary

SpaceXAI is seeking an ML Infrastructure Engineer to design and scale a high-performance ML platform powering recommendations on X. You will build GPU compute infrastructure, data pipelines, and tooling to enable rapid hypothesis testing and large-scale experimentation.

You will collaborate with ML teams to productionize models, ensure reliable integration across the stack, and drive scalability and efficiency across systems.

Qualifications

• Bachelor, Master, Post-graduate or PhD in computer science, machine learning, or other quantitative discipline; or equivalent work experience
• 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

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

Skills

Python

C++

Rust

Distributed systems

Linux

Education

Bachelor/Master/PhD or equivalent

Tools

JAX

PyTorch

NVIDIA drivers

CUDA toolkits

Slurm

Puppet/Ansible

Job description

SpaceXAI is seeking an ML Infrastructure Engineer to design and scale a high-performance ML platform powering recommendations on X. You will build GPU compute infrastructure, data pipelines, and tooling to enable rapid hypothesis testing and large-scale experimentation.

You will collaborate with ML teams to productionize models, ensure reliable integration across the stack, and drive scalability and efficiency across systems.