Job Description
Shape the Future of Intelligence at Quantum Nexus
Quantum Nexus is pioneering the next generation of cognitive computing. We are seeking a visionary Senior Generative AI Engineer to lead our research and deployment efforts in Large Language Models (LLMs), Multimodal AI, and Autonomous Agents. If you are passionate about building systems that reason, create, and interact with the world in a human-like way, we want to meet you.
Why Join Us?
- Impact: Work on projects that redefine human-computer interaction.
- Equity: Competitive equity package for long-term growth.
- Flexibility: Hybrid work model supporting innovation in San Francisco.
Your Mission
You will bridge the gap between cutting-edge research and scalable production engineering. You will architect robust pipelines, fine-tune state-of-the-art models, and ensure our AI systems are safe, ethical, and performant at scale.
Responsibilities
- Model Development: Design, train, and fine-tune LLMs and diffusion models using proprietary datasets.
- System Architecture: Design scalable MLOps infrastructure to handle high-throughput inference and training.
- RAG & Agents: Implement Retrieval-Augmented Generation (RAG) architectures and build autonomous AI agents for complex decision-making tasks.
- Performance Optimization: Optimize model latency and throughput using techniques like quantization, distillation, and hardware acceleration (GPU/TPU).
- Research & Experimentation: Stay ahead of the curve in the AI landscape, evaluating new architectures and benchmarks.
- Code Review & Mentorship: Lead technical discussions, review code, and mentor junior engineers and data scientists.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related technical field.
- Experience: 5+ years of professional experience in AI/ML, with specific expertise in deep learning frameworks.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow; experience with Hugging Face Transformers and LangChain.
- Production Maturity: Demonstrated history of deploying ML models to production environments (AWS, GCP, or Azure).
- Mathematical Fluency: Strong understanding of linear algebra, calculus, and probability.
- Communication: Ability to articulate complex technical concepts to non-technical stakeholders.