ML Infra Engineer: GPU Compute & Pipelines

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

Benefits offered by this job

Equity

Medical, vision, dental

401(k)

Disability insurance

Life insurance

Employee discounts

Job summary

SpaceXSI is seeking an ML Infrastructure Engineer to design, build, and optimize a high-performance ML platform powering recommendations. You will scale GPU compute, data pipelines, and experimentation tooling, collaborating with ML teams to productionize models and ensure reliable, scalable systems.

Ideal candidates have 2+ years in large-scale environments, strong Python (plus C++/Rust), and experience with ML frameworks like JAX or PyTorch.

Qualifications

• Bachelor, Master, Post-graduate or PhD in computer science, ML, or quantitative discipline; or equivalent work experience
• 2+ years in high-traffic or large-scale production environments, distributed systems, GPU infra, or DL apps
• 2+ years experience with ML platforms, training infra, or collaboration with modeling/data science teams
• Strong proficiency with Python and experience with C++ or Rust

Responsibilities

• Design, build, and scale GPU compute infrastructure, training frameworks, and experimentation tools
• Develop data pipelines and integrate large-scale data, training, and inference systems
• Collaborate with ML teams to productionize models and ensure seamless integration
• Ensure scalability, reliability, and efficiency of large-scale ML systems
• Work across the full stack to solve complex problems independently
• Mentor junior engineers and contribute to team growth

Skills

Python

C++

Rust

JAX

PyTorch

Linux

Education

Bachelor/Master/PhD in CS or related field

Tools

Slurm

Puppet/Ansible

NVIDIA drivers

CUDA toolkit

Job description

SpaceXSI is seeking an ML Infrastructure Engineer to design, build, and optimize a high-performance ML platform powering recommendations. You will scale GPU compute, data pipelines, and experimentation tooling, collaborating with ML teams to productionize models and ensure reliable, scalable systems.

Ideal candidates have 2+ years in large-scale environments, strong Python (plus C++/Rust), and experience with ML frameworks like JAX or PyTorch.