Job Description
Shape the Future of Intelligence with the 2026 Initiative.
We are on the precipice of a technological singularity, and FutureScale AI is building the engine that will drive us into 2026 and beyond. As a Lead Research Scientist for our 2026 Horizon Initiative, you will not just use existing technology; you will define the paradigms for the next generation of autonomous systems, generative models, and neural architectures.
In this high-impact role, you will collaborate with world-class engineers and ethicists to solve unsolvable problems, pushing the boundaries of what's possible in AI. If you are driven by the prospect of defining the future of technology and are looking for a role that challenges the status quo, we want to hear from you.
Why Join Us?
- Global Impact: Work on projects that will define the AI landscape for the next decade.
- Top-Tier Compensation: Competitive salary and equity packages.
- Autonomy: The freedom to experiment, fail fast, and innovate.
Apply today to become a pioneer in the 2026 era.
Responsibilities
- Architectural Leadership: Spearhead the research and development of proprietary AI models, specifically targeting the 2026 roadmap for advanced reasoning and autonomy.
- Prototyping & Validation: Design and implement experimental frameworks in Python and C++ to validate novel neural architectures before scaling.
- Research Publication: Author and present high-impact research papers at top-tier conferences (NeurIPS, ICML, ICLR) to establish FutureScale AI as a thought leader.
- Cross-Functional Collaboration: Translate complex research findings into actionable technical specifications for the engineering and product teams.
- Mentorship: Mentor junior researchers and PhD candidates, fostering a culture of rigorous scientific inquiry and innovation.
Qualifications
- Education: PhD or Master's degree in Computer Science, Mathematics, Statistics, or a related quantitative field (PhD preferred).
- Experience: 5+ years of experience in applied machine learning research, with a proven track record of publications or impactful patents.
- Technical Proficiency: Deep expertise in PyTorch, TensorFlow, or JAX. Strong proficiency in C++ for high-performance computing.
- Domain Knowledge: Proven experience in Deep Learning, NLP, Computer Vision, or Reinforcement Learning.
- Problem Solving: Demonstrated ability to tackle open-ended, complex problems with no immediate solution.