Job Description
We are on a mission to define the technological landscape of 2026 and beyond. At Nexus Future Tech, we aren't just building software; we are architecting the future of human-AI collaboration. We are seeking a Senior Generative AI Engineer to lead the development of next-generation Large Language Models (LLMs) and autonomous agents.
In this pivotal role, you will bridge the gap between theoretical AI research and scalable production systems. You will work alongside world-class researchers and engineers to push the boundaries of what's possible in natural language processing, computer vision, and multimodal learning.
Why Join Us?
- Future-Proof Career: Work on cutting-edge technology that will define the industry in 2026.
- Equity Package: Competitive stock options to share in our company's exponential growth.
- Top-Tier Benefits: Comprehensive health, dental, and vision coverage, plus a 401(k) match.
- Flexible Environment: Hybrid work model with a state-of-the-art office in the heart of San Francisco.
Responsibilities
- Model Architecture: Design and implement advanced Generative AI architectures, including transformers, diffusion models, and reinforcement learning from human feedback (RLHF) pipelines.
- Optimization & Scaling: Optimize LLM inference and training throughput using techniques like quantization, pruning, and distributed training across GPU clusters.
- MLOps Implementation: Build robust CI/CD pipelines for machine learning, ensuring model reproducibility and seamless deployment to cloud environments (AWS/GCP).
- Ethical AI: Develop and enforce guidelines for bias mitigation, safety protocols, and responsible AI usage in consumer-facing products.
- Research Integration: Translate cutting-edge academic research into practical, high-performance production code.
- Team Leadership: Mentor junior engineers and data scientists, conducting code reviews and architectural planning sessions.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, Mathematics, or a related technical field.
- Experience: 5+ years of professional experience in software engineering or machine learning, with a specific focus on Deep Learning and NLP.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and experience fine-tuning open-source models (e.g., Llama, Mistral, GPT).
- System Design: Strong understanding of distributed systems, cloud infrastructure, and containerization (Docker, Kubernetes).
- Mathematical Foundation: Deep understanding of linear algebra, calculus, and probability theory.
- Communication: Excellent verbal and written communication skills; ability to explain complex technical concepts to non-technical stakeholders.