Job Description
Join QuantumLeap Dynamics at the forefront of technological evolution as we pioneer the next generation of AI-driven solutions for 2026. We're seeking a visionary Senior AI Systems Architect to design, implement, and optimize cutting-edge neural networks and quantum-enhanced algorithms. This role offers unparalleled opportunities to shape the future of autonomous systems, predictive analytics, and human-AI collaboration in our state-of-the-art Austin innovation hub.
As a key member of our Advanced Research Division, you'll collaborate with Nobel laureates, MIT alumni, and industry disruptors to transform theoretical AI breakthroughs into scalable, real-world applications. Our competitive benefits include equity options, unlimited PTO, and a $15,000 annual innovation stipend for personal R&D projects.
Responsibilities
- Architect and deploy quantum-inspired AI frameworks for predictive modeling and autonomous decision systems
- Lead cross-functional teams in developing next-gen neural networks with 10x efficiency improvements
- Design ethical AI governance frameworks aligned with 2026 regulatory standards
- Optimize large-scale machine learning pipelines for real-time data processing at petabyte scale
- Drive innovation in human-AI symbiosis interfaces through neuromorphic computing integration
- Collaborate with futurists to identify emerging AI paradigms and competitive opportunities
- Mentor junior researchers through quarterly AI ethics symposiums and technical workshops
Qualifications
- PhD in Computer Science, AI, or Quantum Computing with 8+ years of industry experience
- Proven expertise in transformer architectures, reinforcement learning, and quantum algorithms
- Published research in top-tier AI/ML conferences (NeurIPS, ICML, or equivalent)
- Proficiency in PyTorch/TensorFlow and low-level quantum programming frameworks
- Demonstrated experience architecting production-grade AI systems serving 10M+ users
- Deep understanding of 2026 AI regulatory landscape and ethical AI frameworks
- Strong background in neuromorphic computing and brain-computer interfaces