Job Description
Join NeuroSync Labs at the forefront of artificial intelligence innovation. We're pioneering breakthroughs in quantum neural networks and autonomous systems that will redefine technology by 2026. As an AI Research Scientist, you'll collaborate with Nobel laureates and MIT alumni to develop scalable machine learning solutions for Fortune 500 clients. Our state-of-the-art facility in San Francisco offers unmatched resources including quantum computing clusters and petabyte-scale datasets.
We provide competitive equity packages, flexible remote work options, and dedicated R&D budgets for your experimental projects. Shape the future of AI while advancing your career in an environment that celebrates intellectual curiosity.
Responsibilities
- Design and implement novel deep learning architectures for autonomous decision-making systems
- Lead cross-functional teams in developing ethical AI frameworks compliant with 2026 regulatory standards
- Publish breakthrough research in top-tier journals (NeurIPS, ICML, Nature AI)
- Translate theoretical models into production-ready solutions using PyTorch and TensorFlow
- Collaborate with quantum computing teams to hybridize classical and quantum neural networks
- Secure $5M+ in annual research grants through NSF and DARPA partnerships
- Mentor PhD candidates and lead university-industry internship programs
Qualifications
- PhD in Computer Science, Machine Learning, or related field (or equivalent industry experience)
- 5+ years of experience in deep learning research with peer-reviewed publications
- Expertise in transformer architectures, reinforcement learning, and generative models
- Proficiency in Python, C++, and distributed computing frameworks (Ray, Horovod)
- Demonstrated success in deploying ML models at scale (10M+ inference requests/day)
- Strong background in ethical AI development and bias mitigation techniques
- Experience securing federal research grants and managing academic-industry partnerships
- Published work in top-tier AI conferences or journals (h-index > 15 preferred)