Job Description
Join QuantumLeap Systems at the forefront of 2026's technological revolution! We're seeking a visionary AI/ML Infrastructure Engineer to architect next-generation systems that power autonomous AI agents and quantum-enhanced machine learning. Shape the future by building scalable, resilient infrastructure that bridges classical computing and emerging paradigms. Work alongside Nobel-caliber researchers in our state-of-the-art facility with unlimited R&D resources and a culture that celebrates bold innovation.
Why QuantumLeap? We pioneered the first commercial quantum neural network and now lead the charge in creating ethical AI frameworks. Our engineers enjoy equity grants, flexible hybrid work, and exclusive access to our 2026 Innovation Lab.
Responsibilities
- Design and implement distributed ML pipelines for terascale datasets using Kubernetes and GPU orchestration
- Develop quantum-classical hybrid computing architectures for real-time inference
- Optimize infrastructure for energy-efficient AI training with carbon-neutral data centers
- Create automated MLOps pipelines with zero-downtime deployment strategies
- Lead cross-functional teams in deploying federated learning systems across global edge networks
- Architect secure-by-design frameworks for generative AI compliance
- Pioneer novel fault-tolerant computing solutions for mission-critical AI applications
Qualifications
- 5+ years in cloud-native ML infrastructure (AWS/GCP/Azure) with Kubernetes mastery
- Expertise in distributed computing frameworks (Ray, Spark, Horovod)
- Strong background in quantum computing concepts or quantum-inspired algorithms
- Proven experience building carbon-efficient AI systems
- Advanced knowledge of GPU/TPU optimization and heterogeneous computing
- Certification in quantum computing (IBM Qiskit, Google Cirq) or equivalent
- Published research in top-tier ML conferences (NeurIPS, ICML, or arXiv)
- Fluency in Python/Rust with experience in low-level system programming