Job Description
Are you ready to architect the future of intelligence?
Vertex Future Tech is pioneering the next generation of artificial intelligence, specifically designing systems optimized for the technological landscape of 2026 and beyond. We are looking for a visionary Senior AI Engineer to lead our research division in San Francisco. In this role, you will not just build models; you will define the standards for the future of human-machine interaction.
We are a fast-paced, elite engineering team focused on scaling Large Language Models (LLMs) and building autonomous agents. If you are obsessed with performance, ethics in AI, and building systems that scale, we want to meet you.
Responsibilities
- Architect Future-Proof Systems: Design and implement cutting-edge Machine Learning architectures that are scalable, efficient, and ready for the rapid advancements of 2026.
- Model Development: Lead the research and development of proprietary Large Language Models, focusing on reasoning, context retention, and multimodal capabilities.
- MLOps Implementation: Build robust CI/CD pipelines for machine learning, ensuring models are deployed reliably and updated continuously without downtime.
- Cross-Functional Leadership: Collaborate closely with product managers, data scientists, and software engineers to translate complex AI concepts into user-friendly products.
- Research & Innovation: Stay ahead of the curve on emerging AI trends (e.g., Neural Interfaces, Generative Adversarial Networks) and integrate them into our core technology stack.
- Performance Optimization: rigorously optimize inference speed and cost-efficiency for production environments.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field with a focus on AI/ML.
- Experience: 5+ years of professional experience in Machine Learning engineering, with at least 2 years in a senior or lead capacity.
- Technical Stack: Deep proficiency in Python, PyTorch, TensorFlow, or JAX.
- Expertise: Strong background in Deep Learning, Natural Language Processing (NLP), and Reinforcement Learning.
- Infrastructure: Experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
- Soft Skills: Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.