Job Description
Join the Frontier of Intelligence.
We are seeking a visionary Senior AI Engineer to architect the next generation of generative models at Quantum Horizon AI. If you are passionate about pushing the boundaries of Large Language Models (LLMs), Neural Architecture Search, and autonomous agents, we want to meet you.
In this role, you will not just write code; you will define the future of human-machine interaction. You will work in a high-performance environment where innovation is the currency, and your contributions will shape the trajectory of AI technology for the next decade.
Why Quantum Horizon?
- Work with state-of-the-art GPU clusters and cutting-edge frameworks.
- Competitive compensation package including equity.
- Remote-first culture with a focus on deep work and collaboration.
Responsibilities
- Model Architecture & Training: Design, train, and fine-tune state-of-the-art foundation models, specifically focusing on LLMs and multimodal systems.
- R&D Leadership: Lead internal research initiatives to explore novel architectures, including reinforcement learning from human feedback (RLHF) and retrieval-augmented generation (RAG).
- MLOps Implementation: Build scalable, reliable, and secure deployment pipelines using Kubernetes, Docker, and cloud-native services (AWS/GCP).
- Performance Optimization: Optimize model inference speed and memory efficiency to support real-time applications at scale.
- Code Review & Mentorship: Provide technical leadership to junior engineers, conducting rigorous code reviews and fostering a culture of continuous learning.
- Ethical AI: Ensure AI systems adhere to safety guidelines, bias mitigation protocols, and ethical standards.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field, with a focus on AI/ML.
- Experience: 5+ years of professional experience in machine learning, deep learning, or NLP.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX. Strong understanding of Transformer architectures.
- Infrastructure: Experience deploying models on cloud platforms (AWS/GCP/Azure) and familiarity with MLOps tools (MLflow, Kubeflow, Sagemaker).
- Programming: Solid scripting skills in C++ or CUDA for high-performance computing tasks.
- Communication: Exceptional ability to translate complex technical concepts for both technical and non-technical stakeholders.