Job Description
Join the Frontier of Intelligence at Nexus Future Labs
We are seeking a visionary Senior Generative AI Engineer to lead the development of next-generation Large Language Models (LLMs) and multimodal AI systems. If you are passionate about pushing the boundaries of what is possible with artificial intelligence and want to build tools that redefine human-machine interaction, we want to hear from you.
In this role, you will architect, train, and deploy state-of-the-art models at scale. You will work closely with a world-class team of researchers, engineers, and product designers to solve complex problems in natural language understanding, reasoning, and creative generation.
Why Join Us?
- Impactful Work: Your code will power applications used by millions.
- Top-Tier Team: Collaborate with industry leaders in AI and Machine Learning.
- Flexible Environment: Hybrid work model based in the heart of San Francisco.
Key Responsibilities:
Responsibilities
- Model Development: Design, implement, and optimize generative models (e.g., GPT-style architectures, diffusion models) using PyTorch and TensorFlow.
- Training Infrastructure: Build and maintain scalable training pipelines for large-scale model fine-tuning and pre-training.
- Performance Optimization: Reduce inference latency and improve model accuracy through techniques like quantization, pruning, and distillation.
- Research Integration: Stay abreast of the latest academic research in NLP and apply novel techniques to production environments.
- MLOps: Implement robust CI/CD pipelines for model deployment and monitoring using cloud platforms (AWS/GCP).
- Collaboration: Partner with cross-functional teams to define product requirements and translate them into technical solutions.
Qualifications
- Education: MS or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in machine learning, deep learning, or natural language processing.
- Technical Skills: Proficiency in Python, C++, and SQL. Strong knowledge of deep learning frameworks (PyTorch, TensorFlow, JAX).
- LLM Expertise: Deep understanding of Transformer architectures, attention mechanisms, and prompt engineering.
- Deployment: Experience with cloud infrastructure (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
- Communication: Excellent written and verbal communication skills; ability to explain complex technical concepts to non-technical stakeholders.