Job Description
Are you ready to shape the intelligence of tomorrow? Nexus Horizon AI is seeking a visionary Senior AI Engineer to lead our next generation of autonomous agents and multimodal Large Language Models (LLMs).
We are pioneering the future of Generative AI, focusing on ethical, scalable, and high-performance models that redefine human-machine interaction. You will work at the intersection of deep learning, system architecture, and real-world application deployment. If you are passionate about building the technological foundation for 2026 and beyond, this is your opportunity to make a significant impact.
Why Join Us?
- Work with state-of-the-art technology and cutting-edge research.
- Competitive compensation and equity package.
- Flexible remote-first culture with a hub in San Francisco.
Core Responsibilities
- Model Architecture: Design, train, and fine-tune large-scale transformer models and diffusion architectures.
- System Optimization: Engineer efficient inference pipelines and optimize model latency for real-time applications.
- Research & Development: Explore novel approaches in Multimodal AI and Agentic workflows to stay ahead of industry trends.
- Deployment: Manage the full ML lifecycle from experimentation to production deployment on cloud infrastructure (AWS/GCP).
- Cross-Functional Collaboration: Partner with product managers and engineers to integrate AI capabilities into seamless user experiences.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related technical field.
- Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or NLP.
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, and Hugging Face Transformers.
- Infrastructure: Experience with vector databases (Pinecone, Milvus) and RAG (Retrieval-Augmented Generation) architectures.
- Communication: Ability to translate complex technical concepts for non-technical stakeholders.
Responsibilities
- Design and implement scalable AI models and deep learning architectures.
- Conduct cutting-edge research to improve model accuracy and efficiency.
- Collaborate with data scientists to preprocess and augment datasets.
- Optimize existing models for lower latency and higher throughput.
- Ensure all AI systems adhere to ethical guidelines and data privacy regulations.
- Document code, methodologies, and architecture decisions for the engineering team.
Qualifications
- Bachelor’s degree in Computer Science or equivalent practical experience.
- Deep understanding of neural networks, backpropagation, and optimization algorithms.
- Experience with cloud platforms (AWS, Azure, or GCP) and containerization (Docker/Kubernetes).
- Familiarity with MLOps tools such as MLflow or Kubeflow.
- Strong problem-solving skills and a passion for learning new technologies.