Job Description
Are you ready to architect the intelligence of tomorrow? Quantum Horizon Labs is seeking a visionary Senior AI/ML Architect to lead our research into next-generation Artificial General Intelligence (AGI). As we prepare for our 2026 deployment roadmap, we are looking for a technical leader who can bridge the gap between theoretical breakthroughs and scalable production systems.
In this role, you will spearhead the development of multimodal large language models and autonomous agent frameworks. You will work in a high-performance environment where your code directly influences the future of human-machine interaction. Join us in building the foundational technologies that will define the technological landscape of 2026 and beyond.
Why Join Us?
- Future-Ready Tech Stack: Work with the latest advancements in Generative AI, Transformers, and Reinforcement Learning.
- Impact: Your work will solve complex problems that were previously considered impossible.
- Competitive Compensation: Industry-leading salary and equity package.
Responsibilities
- Design and deploy scalable, high-performance deep learning architectures tailored for 2026 enterprise needs.
- Lead the end-to-end lifecycle of AI model development, from data curation and training to fine-tuning and evaluation.
- Optimize model inference latency and throughput for real-time, edge, and cloud deployment environments.
- Collaborate with cross-functional teams of researchers, engineers, and product managers to translate business requirements into technical AI solutions.
- Establish best practices for MLOps, model monitoring, and data governance to ensure robust, reliable AI systems.
- Pioneer research into novel AI paradigms, such as Neuromorphic Computing or Quantum-Inspired Machine Learning.
- Ensure AI systems are ethical, transparent, and aligned with regulatory standards for AI safety.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related technical field.
- 5+ years of professional experience in Machine Learning, Deep Learning, or AI Engineering.
- Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
- Proven track record of deploying large-scale NLP models (e.g., BERT, GPT, Llama) in production environments.
- Strong understanding of distributed systems, cloud infrastructure (AWS, GCP, or Azure), and containerization technologies (Docker, Kubernetes).
- Deep knowledge of statistical analysis, linear algebra, and optimization algorithms.
- Experience with MLOps tools (MLflow, Kubeflow, SageMaker) and data versioning (DVC).