Job Description
Are you ready to architect the artificial intelligence systems that will define the era of 2026 and beyond? Nexus Future Systems is seeking a visionary Senior AI Engineer to join our elite research division in San Francisco. We are building the infrastructure for next-generation generative models and autonomous agents, and we need a technical leader who thrives on solving unsolved problems.
In this role, you will bridge the gap between theoretical machine learning research and production-grade deployment. You will work directly with our Chief Scientist to optimize large language models (LLMs) and computer vision pipelines for high-scale enterprise applications.
Why Join Us?
- Work on cutting-edge AI technology that is shaping the future.
- Competitive compensation package including stock options.
- Flexible work environment in the heart of San Francisco's tech district.
Responsibilities
- Model Architecture: Design, train, and fine-tune state-of-the-art AI models, including Transformers and diffusion models, focusing on performance and scalability.
- Production Deployment: Deploy AI models into high-availability production environments using Kubernetes and Docker, ensuring low-latency inference.
- Optimization: Implement quantization, pruning, and distillation techniques to optimize model efficiency for edge devices.
- Data Pipeline Management: Collaborate with data engineering teams to build robust data pipelines that support continuous model retraining.
- Research Integration: Translate academic research papers into practical, production-ready code and evaluate emerging AI methodologies.
- Mentorship: Guide junior engineers and data scientists, fostering a culture of technical excellence and innovation.
Qualifications
- Education: MS or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Experience: 5+ years of experience in building and deploying machine learning systems at scale.
- Knowledge: Deep understanding of neural network architectures, distributed computing, and cloud infrastructure (AWS/GCP).
- Communication: Excellent written and verbal communication skills, capable of presenting complex technical concepts to diverse audiences.
- Problem Solving: Proven track record of tackling complex optimization problems in AI/ML.