Job Description
At 2026, we are not merely predicting the future; we are architecting it. We are a cutting-edge research lab dedicated to pushing the boundaries of Artificial General Intelligence and Quantum Computing. Our mission is to create sustainable, ethical, and powerful AI systems that redefine human potential.
We are seeking a visionary Senior AI Research Engineer to join our elite team in San Francisco. In this pivotal role, you will spearhead the development of next-generation neural architectures, optimizing them for unprecedented performance and scalability. You will operate at the intersection of theoretical research and practical application, working in a collaborative environment that values creativity, intellectual rigor, and technical excellence.
If you are passionate about solving the hardest problems in machine learning and want to leave a lasting impact on the industry, we want to hear from you.
Responsibilities
- Architect Next-Gen Models: Design and implement state-of-the-art machine learning algorithms and neural network architectures.
- Research Leadership: Conduct extensive research on deep learning paradigms, including Transformers, Graph Neural Networks, and diffusion models.
- Optimization: Optimize existing models for inference speed and memory efficiency on edge devices and large-scale cloud infrastructure.
- Collaborative Development: Work closely with cross-functional teams of data scientists, software engineers, and product managers to translate research into production-ready solutions.
- Knowledge Sharing: Author high-impact technical papers and contribute to open-source repositories to advance the broader community.
- Mentorship: Mentor junior researchers and provide technical guidance on best practices in model training, evaluation, and deployment.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, Physics, or a related quantitative field.
- Experience: Minimum of 5 years of professional experience in deep learning or artificial intelligence research.
- Technical Skills: Strong proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX).
- Research Track Record: A proven track record of publishing at top-tier conferences (NeurIPS, ICML, ICLR) or achieving comparable industry benchmarks.
- Infrastructure: Experience with distributed training systems (Ray, Horovod) and cloud platforms (AWS, GCP, or Azure).
- Theory: Deep understanding of statistical learning theory, optimization techniques, and mathematical foundations of AI.