Job Description
Are you ready to architect the digital landscape of tomorrow? Nexus Future Tech is looking for a visionary Senior AI Engineer to join our elite team in San Francisco. As we race toward the technological milestones of 2026, we need a specialist who can bridge the gap between theoretical AI and scalable, production-grade solutions.
In this role, you will be at the forefront of Generative AI, Large Language Models (LLMs), and autonomous decision-making systems. You won't just be maintaining systems; you will be building the infrastructure that powers the next generation of intelligent applications. If you thrive in a fast-paced, high-impact environment and are passionate about the future of technology, we want to meet you.
Why Join Us?
- Impactful Work: Directly influence the roadmap for our flagship 2026 product suite.
- Innovation First: Access to bleeding-edge hardware and GPU clusters for research.
- Competitive Compensation: Top-of-market salary and equity package.
- Flexible Culture: Remote-first flexibility with a vibrant SF office culture.
Responsibilities
- Design, train, and deploy advanced machine learning models, specifically focusing on LLMs and Generative AI architectures.
- Optimize model inference latency and accuracy to ensure seamless user experiences in real-time applications.
- Build and maintain robust MLOps pipelines for continuous integration, training, and deployment of AI models.
- Collaborate closely with data scientists and product managers to translate complex technical requirements into scalable engineering solutions.
- Conduct rigorous testing, validation, and monitoring of AI models to ensure compliance with ethical AI standards and safety guidelines.
- Research and prototype novel algorithms to stay ahead of industry trends and technological advancements.
Qualifications
- PhD or Master's degree in Computer Science, Mathematics, or a related quantitative field (or equivalent practical experience).
- 5+ years of professional experience in software engineering with a strong focus on Machine Learning or Artificial Intelligence.
- Proficiency in Python and deep knowledge of ML frameworks such as TensorFlow, PyTorch, or JAX.
- Proven experience in developing, fine-tuning, and deploying LLMs and NLP models.
- Strong understanding of cloud infrastructure (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Experience with vector databases (e.g., Pinecone, Milvus) and RAG (Retrieval-Augmented Generation) architectures.