Job Description
Are you ready to define the intelligence of the next decade?
Nexus Future Systems is seeking a visionary Senior AI Architect to spearhead our breakthrough research and engineering efforts leading up to our 2026 AGI Launch. We are building the infrastructure that will power the autonomous systems of tomorrow, and we need a technical leader who thrives on complexity and innovation.
In this pivotal role, you will bridge the gap between theoretical machine learning research and scalable production systems. You will be instrumental in defining the architectural blueprints for our 2026 roadmap, ensuring our models are not only state-of-the-art but also safe, efficient, and responsible.
Why Nexus Future Systems?
- Work on the frontier of Artificial General Intelligence.
- Competitive compensation and equity package.
- Flexible remote-first culture with a hub in San Francisco.
Responsibilities
- Architectural Leadership: Design and implement the core machine learning infrastructure required for our 2026 product line, ensuring high scalability and fault tolerance.
- AGI Research: Lead the research into novel neural network architectures and self-learning algorithms to accelerate our path toward AGI.
- Team Mentorship: Mentor a team of top-tier engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Roadmap Strategy: Collaborate with the CTO and product teams to translate scientific breakthroughs into concrete engineering milestones.
- Model Optimization: Oversee the deployment and optimization of large-scale models, focusing on inference latency and resource efficiency.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, or a related field, with a focus on AI, Machine Learning, or Computational Neuroscience.
- Experience: 8+ years of experience in software engineering and machine learning, with at least 3 years in a senior architectural or lead role.
- Technical Stack: Deep expertise in PyTorch, TensorFlow, or JAX, and experience with distributed training systems (Ray, Kubernetes).
- Specialized Knowledge: Strong background in Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning is highly preferred.
- Problem Solving: Proven track record of solving complex, ambiguous problems and delivering robust solutions in high-pressure environments.