Job Description
Are you ready to define the future of Artificial Intelligence? Nexus Future Systems is launching The 2026 Initiative, a groundbreaking research program dedicated to engineering the next generation of autonomous, multimodal AI systems. We are seeking a visionary Senior AI Research Engineer to lead our core development team in San Francisco.
In this role, you won't just build models; you will architect the cognitive frameworks that will power the enterprise landscape of 2026 and beyond. You will work in a high-performance environment with top-tier talent, pushing the boundaries of what is possible in Large Language Models (LLMs), reinforcement learning, and ethical AI deployment.
Why join The 2026 Initiative?
- Impact: Your work will directly shape the technological trajectory of the next decade.
- Autonomy: We offer significant autonomy to define research directions and methodologies.
- Equity: Competitive equity package for early-stage contributors.
Responsibilities
- Lead R&D: Spearhead the architecture and training of proprietary Large Language Models optimized for the 2026 deployment cycle.
- Scalability: Design and implement distributed training pipelines capable of handling exabytes of data with zero downtime.
- Model Optimization: Push the limits of inference speed and efficiency on heterogeneous hardware (GPUs/TPUs) to enable real-time AI applications.
- Ethical AI: Integrate robust safety alignment and bias mitigation protocols into the core model architecture.
- Cross-Functional Leadership: Collaborate with software engineers, product managers, and data scientists to translate research breakthroughs into production-ready software.
- Publication: Author and present research findings at top-tier conferences (NeurIPS, ICML, ICLR) to establish industry thought leadership.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Machine Learning, Mathematics, or a related quantitative field.
- Experience: 5+ years of professional experience in AI/ML research or a comparable research environment.
- Technical Stack: Deep proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with Hugging Face, Ray, or Kubernetes is highly desirable.
- Research Background: A strong publication record in machine learning (ArXiv, conferences) and a deep understanding of transformer architectures, attention mechanisms, and diffusion models.
- Problem Solving: Proven ability to tackle complex, unsolved problems in natural language understanding and generation.
- Communication: Excellent technical writing and verbal communication skills for both internal collaboration and external public speaking.