Job Description
Are you ready to define the technological landscape of 2026? Nexus Future Tech is seeking a visionary Senior AI/ML Engineer to lead our next-generation generative intelligence initiatives. We are building the foundational models that will power the autonomous systems of the future. In this pivotal role, you will bridge the gap between theoretical research and scalable production engineering, ensuring our AI solutions are not only cutting-edge but ethically responsible and robust.
Join a diverse team of pioneers committed to pushing the boundaries of what is possible in artificial intelligence. You will work in a state-of-the-art environment that values innovation, speed, and impact.
Why Join Us?
- Shape the Future: Be at the forefront of AI development leading up to 2026.
- Impactful Work: Deploy models that directly influence millions of users worldwide.
- Competitive Compensation: Industry-leading salary and equity packages.
- Flexible Environment: Hybrid work model based in the heart of San Francisco.
Responsibilities
- Design, develop, and deploy scalable machine learning pipelines and algorithms tailored for future-proof applications.
- Research and implement state-of-the-art techniques in Large Language Models (LLMs) and multimodal learning.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate business requirements into technical solutions.
- Optimize model performance for inference speed and memory efficiency in high-volume production environments.
- Ensure data quality, model fairness, and adherence to ethical AI guidelines in all deployed systems.
- Mentor junior engineers and conduct code reviews to maintain high engineering standards.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related quantitative field.
- 5+ years of professional experience in Machine Learning or Artificial Intelligence.
- Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of deep learning architectures, NLP, and computer vision.
- Experience with MLOps tools (Kubeflow, MLflow, Airflow) and cloud platforms (AWS, GCP, or Azure).
- Proven track record of shipping production-grade ML models.
- Excellent communication skills and ability to explain complex technical concepts to non-technical stakeholders.