Job Description
Are you ready to define the technology of tomorrow? Nebula Core Systems is seeking a visionary Senior AI Architect to lead our cutting-edge Artificial General Intelligence (AGI) initiative. We are building the cognitive engines that will power the next decade of human-machine interaction, and we need a pioneer to architect the systems that will define the year 2026 and beyond.
In this role, you will bridge the gap between theoretical research and scalable production systems. You will design neural architectures that exhibit emergent reasoning capabilities, ensuring our AI is not only powerful but also safe, ethical, and aligned with human values. If you are passionate about the future of technology and want to leave a lasting legacy, we want to hear from you.
Why Join Us?
We offer a competitive compensation package, equity options, and a remote-first culture that values deep work and innovation. You will work with a team of world-class researchers and engineers dedicated to solving the hardest problems in machine consciousness.
Responsibilities
- Architect Next-Gen Neural Networks: Design and implement proprietary deep learning architectures capable of handling complex, multi-modal reasoning tasks.
- Lead Model Development: Oversee the end-to-end lifecycle of large-scale AI models, from training data curation to inference optimization.
- Ethical AI Governance: Establish and enforce safety protocols and ethical guidelines to prevent hallucinations and ensure alignment with human intent.
- System Scalability: Engineer infrastructure that can handle billions of parameters while maintaining low latency and high throughput.
- Collaborate Across Disciplines: Work closely with neuroscientists, cognitive psychologists, and software engineers to refine our cognitive models.
- Patent & Publish: Lead research efforts aimed at publishing breakthrough papers and filing patents for novel AI methodologies.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Cognitive Science, or a related field.
- Experience: 5+ years of professional experience in machine learning engineering, with a focus on NLP, Reinforcement Learning, or Multi-Agent Systems.
- Technical Skills: Proficiency in PyTorch, TensorFlow, and distributed computing frameworks (e.g., Ray, Kubernetes).
- Domain Knowledge: Deep understanding of transformer architectures, attention mechanisms, and emergent behavior in LLMs.
- Problem Solving: Proven ability to troubleshoot complex, undefined technical problems and derive innovative solutions.
- Communication: Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to diverse stakeholders.