Job Description
Join QuantumLeap Dynamics at the forefront of 2026's technological revolution! We're pioneering next-generation AI systems that will redefine industries, and we need visionary engineers to build the future. This role offers unparalleled opportunities to work on bleeding-edge projects including quantum machine learning, autonomous systems, and ethical AI frameworks. Collaborate with Nobel laureates and disruptors in our state-of-the-art San Francisco lab, where your innovations will directly shape human progress.
We provide comprehensive benefits including equity, flexible work arrangements, and dedicated R&D time. Our culture values intellectual curiosity and bold experimentation—mistakes are celebrated as stepping stones to breakthroughs. If you're driven to solve humanity's most complex challenges through technology, this is your calling.
Responsibilities
- Architect and deploy production-grade ML models for real-time decision-making systems
- Lead research initiatives in quantum-enhanced neural networks and federated learning
- Develop ethical AI frameworks ensuring bias mitigation and regulatory compliance
- Collaborate with quantum computing teams to hybridize classical and quantum ML approaches
- Mentor junior engineers and publish breakthrough research in top-tier AI conferences
- Optimize edge-computing solutions for autonomous systems with 99.999% reliability
- Drive innovation in explainable AI (XAI) techniques for high-stakes domains
Qualifications
- PhD or Master's in Computer Science/AI with 5+ years of ML engineering experience
- Expertise in PyTorch/TensorFlow and distributed computing frameworks
- Proven track record of deploying AI systems at scale in regulated industries
- Deep knowledge of quantum computing principles and quantum algorithms
- Strong background in reinforcement learning and multi-agent systems
- Published research in NeurIPS/ICML/ICLR or equivalent tier conferences
- Experience with MLOps pipelines and cloud-native AI infrastructure (AWS/GCP)
- Demonstrated ability to translate complex research into production-ready solutions