Job Description
Are you ready to define the technological landscape of 2026? Nexus Future Labs is on the hunt for a visionary Senior Generative AI Engineer to join our elite R&D team. In this role, you won't just implement existing models; you will architect the next generation of intelligent systems that push the boundaries of artificial general intelligence.
We are building the future of work and creativity. If you are passionate about Large Language Models (LLMs), fine-tuning strategies, and multimodal architectures, this is your stage. You will work in a high-performance environment focused on ethical AI, scalability, and groundbreaking innovation.
Why join us?
- Impactful Work: Directly influence the roadmap for 2026 and beyond.
- Top-tier Compensation: Competitive salary and equity package.
- Cutting-edge Tech: Access to the latest GPUs and cloud infrastructure.
- Culture: A diverse, remote-first culture that values bold ideas and continuous learning.
Responsibilities
- Architect & Deploy: Design and implement scalable Generative AI architectures, including LLMs and diffusion models, deployed on robust cloud infrastructure.
- Model Optimization: Drive research and engineering efforts to improve model accuracy, reduce inference latency, and optimize token generation speed.
- RAG & Fine-Tuning: Develop and maintain Retrieval-Augmented Generation (RAG) pipelines to ensure factual accuracy and reduce hallucinations.
- Research & Development: Stay ahead of the curve on emerging AI trends (e.g., Multimodal AI, Agentic workflows) to integrate them into our product suite.
- Code Quality: Mentor junior engineers and establish best practices for reproducible research and production-grade code.
- Cross-functional Collaboration: Partner with product managers, data scientists, and designers to translate complex AI capabilities into user-friendly applications.
Qualifications
- Education: MS or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in software engineering or AI research, with a focus on deep learning.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with Hugging Face Transformers is highly preferred.
- LLM Expertise: Deep understanding of transformer architectures, pre-training, fine-tuning, and prompt engineering.
- Infrastructure: Experience deploying models on AWS, GCP, or Azure using Kubernetes and Docker.
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders.