Job Description
Join QuantumLeap Systems at the forefront of 2026's technological revolution! We're seeking an AI/ML Infrastructure Engineer to architect and deploy next-generation systems that will power the next wave of artificial intelligence breakthroughs. This role is critical in building scalable, resilient infrastructure for our autonomous systems division, where you'll work with cutting-edge quantum-inspired algorithms and edge-computing frameworks.
Our ideal candidate thrives in ambiguity and possesses deep expertise in optimizing ML pipelines for trillion-parameter models. You'll collaborate with world-class researchers to translate theoretical advances into production-ready systems that push the boundaries of what's possible in AI. If you're passionate about shaping the technological landscape of 2026, this is your moment.
Responsibilities
- Design and implement distributed ML training pipelines for exascale models
- Optimize GPU/TPU clusters for quantum-inspired neural networks
- Architect hybrid-cloud infrastructure supporting edge-to-cloud AI deployment
- Develop MLOps frameworks for autonomous system lifecycle management
- Lead security-first implementations for federated learning systems
- Create performance benchmarks for next-gen AI hardware acceleration
- Maintain 99.999% uptime for critical AI inference services
Qualifications
- 5+ years in ML infrastructure with production-level deep learning systems
- Expertise in Kubernetes, Ray, and Kubeflow orchestration
- Advanced knowledge of GPU/TPU optimization (CUDA, ROCm)
- Experience with quantum computing APIs and hybrid algorithms
- Strong background in distributed systems and fault-tolerant architectures
- Proficiency in Python, Go, and Rust for high-performance computing
- PhD or equivalent in Computer Science/Engineering preferred
- Published research at NeurIPS/ICML/ICLR in the last 3 years