ML Infrastructure Engineer - Scale GPU ML Platforms

SpaceXAI; United States; On-site; USD 180,000 - 440,000; Full time

Benefits offered by this job

Equity

Medical, vision, dental

401(k) retirement plan

Disability insurance

Job summary

SpaceXAI is hiring an ML Infrastructure Engineer to design and scale a high-performance ML platform powering recommendations. You will own GPU compute infrastructure, training frameworks, and experimentation tools to accelerate ML research and production throughput.

You will collaborate with ML teams to productionize models, ensure reliability, and optimize the stack from data ingestion to inference. The role includes mentoring junior engineers and shaping the future of our infrastructure.

Qualifications

• Bachelors/Masters/PhD in CS or quantitative field or equivalent experience (BSc/BEng not required).
• 2+ years in high-traffic or large-scale production environments.
• 2+ years with ML platforms, training infra, or modeling collaboration.
• Strong Python, plus C or Rust is a plus.

Responsibilities

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

Skills

Python programming

Distributed systems

ML platforms

GPU infrastructure

Strong communication

Education

Bachelor/Master/PhD in CS or related field

Tools

CUDA toolkits

NVIDIA drivers

Slurm

Puppet/Ansible

Job description

SpaceXAI is hiring an ML Infrastructure Engineer to design and scale a high-performance ML platform powering recommendations. You will own GPU compute infrastructure, training frameworks, and experimentation tools to accelerate ML research and production throughput.

You will collaborate with ML teams to productionize models, ensure reliability, and optimize the stack from data ingestion to inference. The role includes mentoring junior engineers and shaping the future of our infrastructure.