Job Description
We are Nexus Horizon AI, a cutting-edge research lab pioneering the next generation of artificial intelligence. As we approach our critical 2026 roadmap launch, we are looking for a visionary Lead Generative AI Engineer to architect the models that will define the future of human-computer interaction.
In this role, you will not just implement existing solutions; you will push the boundaries of Large Language Models (LLMs), Multimodal AI, and Reinforcement Learning from Human Feedback (RLHF). You will lead a high-performance team of researchers and engineers, ensuring our proprietary models are scalable, efficient, and ethically aligned.
Join us in shaping the trajectory of AI technology for the decade ahead.
Responsibilities
- Architectural Leadership: Design and implement scalable, high-performance Generative AI architectures tailored for the 2026 product ecosystem.
- Model Development: Spearhead the training and fine-tuning of proprietary LLMs and diffusion models, optimizing for inference speed and accuracy.
- Research & Innovation: Stay at the forefront of the AI frontier, exploring novel techniques in prompt engineering, context window optimization, and synthetic data generation.
- Team Mentorship: Guide a cross-functional team of data scientists and ML engineers, fostering a culture of technical excellence and rapid iteration.
- MLOps Integration: Establish robust CI/CD pipelines and deployment strategies to ensure seamless model rollouts in production environments.
- Strategic Roadmap: Collaborate with product leadership to define technical milestones and deliverables for the upcoming 2026 launch.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field from a top-tier institution.
- Experience: 5+ years of professional experience in building and deploying production-grade machine learning models.
- Technical Skills: Deep expertise in PyTorch, TensorFlow, or JAX. Proficiency in C++ for performance optimization.
- Domain Knowledge: Strong understanding of Transformer architectures, attention mechanisms, and tokenization strategies.
- Tools: Experience with cloud platforms (AWS/GCP/Azure), Docker, Kubernetes, and MLflow or similar MLOps tools.
- Communication: Exceptional ability to translate complex technical concepts for diverse stakeholders and executive audiences.