Job Description
Are you ready to architect the future of artificial intelligence? Nexus Horizon Tech is seeking a visionary Senior AI Engineer to join our elite engineering team in San Francisco. We are building the next generation of Large Language Models (LLMs) and predictive intelligence platforms that will redefine enterprise automation.
In this role, you will bridge the gap between cutting-edge research and production-grade deployment. You will work directly with our CTO to design scalable architectures, optimize inference latency, and implement ethical AI governance standards. If you are passionate about the intersection of data science and software engineering, this is your opportunity to lead.
Why Join Nexus Horizon?
- Work on high-impact projects that scale globally.
- Competitive equity package and comprehensive benefits.
- Flexible remote-first culture with a hub in the heart of SF.
- Access to the latest hardware for AI research.
Key Responsibilities
- Model Development: Design, train, and fine-tune large-scale transformer models using PyTorch and TensorFlow.
- Infrastructure Optimization: Build and maintain high-throughput, low-latency inference pipelines using Kubernetes and Docker.
- MLOps Integration: Implement CI/CD pipelines for model deployment, ensuring automated retraining and A/B testing frameworks.
- Research & Innovation: Stay abreast of the latest academic papers in NLP and Computer Vision, translating them into practical engineering solutions.
- Team Leadership: Mentor junior data scientists and engineers, conducting code reviews and architectural reviews.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field.
- Experience: 5+ years of professional experience in machine learning, deep learning, or natural language processing.
- Technical Skills: Proficiency in Python, PyTorch, Hugging Face Transformers, and SQL.
- Cloud Expertise: Strong experience deploying models on AWS or Google Cloud Platform (GCP).
- Problem Solving: Demonstrated ability to debug complex distributed systems and optimize model performance under production constraints.
Responsibilities
- Design and deploy scalable machine learning models and AI-driven solutions.
- Optimize model inference performance and reduce latency for real-time applications.
- Collaborate with data scientists to preprocess data and improve feature engineering.
- Implement MLOps best practices for continuous integration and deployment (CI/CD).
- Mentor junior engineers and contribute to the technical roadmap.
- Ensure ethical AI practices and data governance compliance.
Qualifications
- Master’s or PhD in Computer Science, AI, or a related technical field.
- Minimum of 5 years of experience in machine learning engineering or data science.
- Strong proficiency in Python, PyTorch, and TensorFlow.
- Experience with cloud platforms (AWS/GCP) and containerization (Docker/Kubernetes).
- Deep understanding of NLP and LLM architectures.
- Excellent communication skills and ability to work in a fast-paced agile environment.