Job Description
Join QuantumLeap Labs at the forefront of AI evolution! We're seeking a visionary AI/ML Infrastructure Engineer to architect next-generation systems that power 2026's intelligent landscape. You'll build scalable, high-performance foundations for cutting-edge AI models while shaping the future of machine learning infrastructure.
As a key innovator in our San Francisco hub, you'll collaborate with elite teams to deploy transformative solutions that redefine industry standards. This role offers unparalleled growth opportunities in one of the world's most dynamic tech ecosystems.
Responsibilities
- Design and implement cloud-native ML pipelines on AWS/GCP for terabyte-scale data processing
- Optimize GPU/TPU clusters for distributed training of next-gen foundation models
- Develop MLOps automation frameworks for model deployment at enterprise scale
- Architect secure, compliant data pipelines for sensitive AI applications
- Lead migration of legacy systems to modern AI infrastructure stacks
- Mentor junior engineers on bleeding-edge ML infrastructure best practices
Qualifications
- 5+ years in ML infrastructure engineering with Kubernetes, Docker, and Terraform
- Expertise in distributed computing frameworks (Spark, Ray) and GPU orchestration
- Proven track record optimizing deep learning training pipelines
- Strong Python proficiency with ML frameworks (PyTorch/TensorFlow)
- Experience with CI/CD for ML systems (MLflow, Kubeflow)
- Relevant certifications (AWS/GCP ML, Kubernetes Administrator)
- Published research in AI infrastructure or ML optimization preferred