Job Description
Are you ready to shape the future of artificial intelligence?
Nexus Future Tech is pioneering the 2026 AI Roadmap, building next-generation Large Language Models (LLMs) and autonomous agents that will redefine human-computer interaction. We are seeking a visionary Senior AI & Machine Learning Architect to lead our research division. You will not just implement existing solutions; you will architect the foundational models that will power our products through 2026 and beyond.
In this role, you will bridge the gap between theoretical research and scalable production systems, ensuring our AI is not only cutting-edge but ethically sound and incredibly efficient.
Why Join Us?
- Work on mission-critical projects with a global impact.
- Competitive compensation package including equity.
- Flexible remote-first culture with HQ in the heart of San Francisco.
- Access to top-tier compute infrastructure and the latest hardware.
Responsibilities
- Architect Scalable ML Systems: Design and implement end-to-end machine learning pipelines capable of handling petabytes of data with sub-millisecond latency.
- Lead Model Research: Drive the research and development of novel neural network architectures specifically tailored for the 2026 technological landscape.
- Optimize Inference: Implement model quantization, pruning, and distillation techniques to reduce computational costs while maintaining accuracy.
- Collaborate with Cross-Functional Teams: Partner with software engineers, data scientists, and product managers to integrate AI models into robust production environments.
- Set Technical Standards: Establish coding standards, documentation practices, and review protocols for the AI engineering team.
- Mentorship: Guide junior engineers and data scientists, fostering a culture of continuous learning and innovation.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field (PhD preferred).
- Experience: 5+ years of professional experience in machine learning engineering or applied research.
- Programming: Deep proficiency in Python, PyTorch, or TensorFlow.
- Infrastructure: Strong experience with cloud platforms (AWS/GCP/Azure) and containerization technologies (Docker/Kubernetes).
- Mathematical Foundation: Solid understanding of linear algebra, calculus, probability, and statistics.
- Soft Skills: Exceptional problem-solving abilities and excellent verbal and written communication skills.