Job Description
Are you ready to architect the intelligence of tomorrow?
Nexus Future Labs is pioneering the next generation of Artificial Intelligence. We are looking for a visionary Senior AI Research Scientist (2026 Horizon) to lead our research initiatives, pushing the boundaries of what is possible in generative models, predictive analytics, and autonomous systems.
In this role, you will not just build models; you will define the roadmap for AI evolution. You will collaborate with cross-functional teams to transform theoretical breakthroughs into scalable, real-world solutions that will shape the industry landscape for years to come.
Why Join Us?
- Work on cutting-edge technology focused on the 2026 AI ecosystem.
- Competitive compensation and equity package.
- Flexible remote-first policy with a premium San Francisco office.
- Access to top-tier compute resources and research grants.
Responsibilities
- Lead the research and development of novel AI architectures aimed at the 2026 technological landscape, focusing on scalability and ethical AI.
- Design, implement, and optimize deep learning models using Python, PyTorch, and TensorFlow.
- Collaborate with product and engineering teams to translate research findings into production-ready APIs and features.
- Publish high-impact research papers in top-tier conferences (NeurIPS, ICML, ICLR) and contribute to open-source communities.
- Mentor junior researchers and data scientists, fostering a culture of innovation and continuous learning.
- Evaluate emerging technologies and frameworks to ensure our stack remains at the forefront of the industry.
Qualifications
- Ph.D. or Master's degree in Computer Science, Artificial Intelligence, or a related quantitative field.
- Minimum of 5 years of experience in research engineering or applied machine learning roles.
- Proven track record of publishing in top AI conferences or delivering measurable business impact through AI.
- Strong proficiency in Python, SQL, and distributed computing frameworks (e.g., Spark, Kubernetes).
- Deep understanding of deep learning principles, including NLP, Computer Vision, or Reinforcement Learning.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.