Senior AI/ML Systems Engineer - Scalable Infra & Automation

Socket.dev; Santa Clara (CA); On-site; USD 150,000 - 170,000; Full time

Benefits offered by this job

Unlimited PTO

Generous parental leave

Entrepreneurial culture

Open communication

Small teams = high impact

Medical, Dental, Vision

Disability & Life insurance

Mental health support

Annual bonus program

ESPP

Team building experiences

Mentorship programs

Manager resources

Job summary

Socket.dev is seeking a Software Engineer focused on AI/ML applications to own the end-to-end lifecycle of AI-powered systems. You will deploy, test, and scale models on distributed GPU infrastructure, while building automated testing and secure CI/CD pipelines.

You will drive experiments, benchmark models, and analyze errors with dashboards to communicate performance to stakeholders. Independent ownership and cross-team collaboration are essential.

Qualifications

• 6+ years of professional experience building production-grade AI/ML systems.

Responsibilities

• Architect and scale AI infrastructure across multi-node clusters using Kubernetes, Ray, or Slurm.
• Design and evaluate production-grade AI models and agents with benchmarking capabilities.
• Run comprehensive model benchmarks and build analytics dashboards for performance.
• Develop extensive automated testing that covers deterministic code and stochastic AI outputs.
• Automate DevSecOps with vulnerability scanning on MR workflows and fast remediation pipelines.
• Own features from ideation to production, including architecture decisions and repo synchronization.
• Collaborate across teams and with open-source communities.

Skills

AI infrastructure

Python (async)

Kubernetes

Ray

Slurm

LangChain

Hugging Face

MLOps

GitLab pipelines

PyTest

Git advanced workflows

Education

Bachelor's or Master's degree in Computer Science, Engineering, or related field

Tools

Kubernetes

Ray

Slurm

LangChain

Hugging Face

MLOps

vLLM

SGLang

PyTest

GitLab

Job description

Socket.dev is seeking a Software Engineer focused on AI/ML applications to own the end-to-end lifecycle of AI-powered systems. You will deploy, test, and scale models on distributed GPU infrastructure, while building automated testing and secure CI/CD pipelines.

You will drive experiments, benchmark models, and analyze errors with dashboards to communicate performance to stakeholders. Independent ownership and cross-team collaboration are essential.