Job Description
We are building the operating system for the next decade. At Nexus Future Labs, we don't just predict the future; we architect it. As a Senior Generative AI Engineer, you will be at the forefront of the 2026 AI revolution, developing scalable Large Language Models and autonomous agents that redefine human-computer interaction.
You will join a world-class team of researchers, engineers, and designers dedicated to solving humanity's most complex problems through advanced artificial intelligence. If you are passionate about the intersection of deep learning, ethics, and scalable software architecture, we want to hear from you.
Why Join Us?
- Work on cutting-edge technology that will shape the future of the industry.
- Competitive compensation and equity packages.
- Unlimited PTO and comprehensive health benefits.
- Flexible remote-first culture with a premium San Francisco office.
Responsibilities
- Architect and Deploy: Design, train, and deploy state-of-the-art Generative AI models tailored for enterprise-scale applications and consumer products.
- Optimization: Optimize model inference for low-latency environments using distributed computing architectures and quantization techniques.
- Research & Innovation: Lead the research and implementation of emerging AI paradigms, including Multimodal learning, Chain-of-Thought reasoning, and Self-Supervised techniques.
- System Integration: Build robust Retrieval-Augmented Generation (RAG) pipelines and vector database architectures to enhance model accuracy and context awareness.
- Ethical AI: Collaborate with the ethics board to integrate safety protocols, bias mitigation strategies, and responsible AI guidelines into production workflows.
- Mentorship: Mentor junior engineers and data scientists, fostering a culture of technical excellence, code quality, and continuous learning.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field.
- Experience: 5+ years of professional experience in software engineering and machine learning.
- Frameworks: Extensive hands-on experience with deep learning frameworks (PyTorch, TensorFlow, or JAX).
- LLMs: Proven track record of training, fine-tuning, and serving Large Language Models (LLMs) and foundation models.
- MLOps: Proficiency in MLOps tools (MLflow, Kubeflow), containerization (Docker, Kubernetes), and cloud infrastructure (AWS, GCP, or Azure).
- NLP: Strong understanding of Natural Language Processing (NLP), transformer architectures, and attention mechanisms.
- Communication: Excellent written and verbal communication skills, with the ability to translate complex technical concepts to diverse stakeholders.