Job Description
Shape the future of artificial intelligence at QuantumLeap Labs. We're seeking a visionary AI/ML Infrastructure Engineer to build scalable systems that will power breakthrough innovations through 2026. Join our elite team in Austin, TX, where you'll architect next-generation machine learning pipelines and deploy cutting-edge AI solutions at enterprise scale.
This role offers unparalleled opportunities to work with quantum computing hybrids, federated learning frameworks, and neuromorphic computing systems. You'll collaborate with Nobel Prize-winning researchers and industry pioneers to solve humanity's most complex challenges.
Responsibilities
- Design and implement distributed ML training infrastructure for petabyte-scale datasets
- Optimize AI model deployment across hybrid cloud/quantum environments
- Develop MLOps pipelines for autonomous model lifecycle management
- Architect federated learning systems for multi-party data collaboration
- Create real-time inference engines for edge and cloud deployment
- Implement security frameworks for AI model protection and privacy
- Lead R&D initiatives in neuromorphic computing and quantum-AI hybrids
Qualifications
- 5+ years in ML infrastructure with Kubernetes, TensorFlow Extended, or equivalent
- Expertise in distributed computing frameworks (Ray, Dask, Spark)
- Experience with quantum computing APIs (Qiskit, Cirq) or neuromorphic systems
- Proficiency in GPU/TPU optimization and low-level programming (CUDA, OpenCL)
- Certification in Google Cloud AI Platform or AWS SageMaker
- Published research in top-tier AI conferences (NeurIPS, ICML, ICLR)
- PhD in Computer Science or related field with ML focus