Job Description
Are you ready to define the future of Artificial Intelligence?
Nexus Horizon is pioneering next-generation generative models and autonomous systems. We are seeking a visionary Senior AI Engineer to join our elite team in San Francisco. In this role, you will architect scalable machine learning pipelines and deploy state-of-the-art models that redefine industry standards.
We offer a competitive compensation package, equity, and a remote-first culture that fosters innovation and creativity. If you are passionate about pushing the boundaries of what is possible with AI, we want to hear from you.
Responsibilities
- Model Architecture & Development: Design, train, and fine-tune advanced deep learning models, including Large Language Models (LLMs) and Computer Vision systems.
- Infrastructure & MLOps: Build and maintain robust MLOps pipelines using Docker, Kubernetes, and cloud platforms (AWS/GCP) to ensure scalability and reliability.
- Performance Optimization: Optimize model inference speeds and reduce latency to deliver real-time AI experiences to millions of users.
- Research & Innovation: Stay at the forefront of AI research, experimenting with novel architectures and techniques to solve complex business problems.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and software engineers to translate technical requirements into scalable product solutions.
- Code Review & Mentorship: Lead code reviews, mentor junior engineers, and contribute to the technical vision of the engineering department.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, or a related field (PhD is a plus).
- Experience: 5+ years of professional experience in machine learning engineering or applied AI research.
- Programming: Strong proficiency in Python (PyTorch, TensorFlow, JAX) and experience with C++ for high-performance computing.
- Tools: Deep understanding of MLOps tools (MLflow, Kubeflow, Airflow) and containerization technologies (Docker, Kubernetes).
- Knowledge: Solid grounding in classical machine learning algorithms and deep learning theory.
- Communication: Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.