Job Description
Are you ready to define the trajectory of Artificial General Intelligence? Nebula Horizon is seeking a visionary Senior Generative AI Architect to lead our 2026 technology roadmap. We are building the next generation of autonomous systems, and we need a leader who understands the intersection of deep learning, distributed systems, and ethical AI.
In this pivotal role, you will design and deploy state-of-the-art Large Language Models (LLMs) and multimodal architectures. You will work directly with top-tier researchers and engineers to push the boundaries of what is possible in Generative AI, ensuring our solutions are scalable, efficient, and aligned with human values.
Why Join Us?
- Work on cutting-edge technology that will define the 2026 era.
- Competitive equity package and top-tier benefits.
- Flexible remote-first culture with hubs in SF and NYC.
Key Objectives:
- Architect robust model pipelines for production deployment.
- Drive research initiatives in RLHF and Chain-of-Thought reasoning.
- Optimize inference latency and reduce operational costs.
Responsibilities
- Model Architecture: Design and implement novel generative model architectures, including transformers, diffusion models, and reinforcement learning agents.
- System Optimization: Engineer high-performance inference systems using TensorRT and ONNX Runtime to ensure sub-millisecond latency.
- Research Implementation: Translate academic research papers into production-ready code and evaluate new SOTA models.
- Collaboration: Partner with product and engineering teams to integrate AI capabilities into consumer-facing applications seamlessly.
- Safety & Ethics: Implement guardrails and safety protocols to mitigate bias and ensure safe AI deployment.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related technical field.
- Experience: 5+ years of experience in machine learning engineering, specifically with LLMs and Generative AI.
- Programming: Proficiency in Python, C++, and CUDA.
- Frameworks: Deep experience with PyTorch, TensorFlow, or JAX.
- Tools: Familiarity with ML Ops tools (MLflow, Kubeflow) and Vector Databases (Pinecone, Milvus).
- Problem Solving: Demonstrated ability to solve complex mathematical and algorithmic problems at scale.