Job Description
We are seeking a visionary Lead AI Research Scientist to architect the next generation of Generative AI systems. At Nexus Future Labs, we are not just building models; we are defining the landscape of Artificial Intelligence for the year 2026 and beyond. You will lead a high-impact team in developing autonomous agents, multi-modal reasoning engines, and scalable foundation models.
In this pivotal role, you will bridge the gap between theoretical breakthroughs and real-world deployment. If you are passionate about pushing the boundaries of LLMs, Reinforcement Learning from Human Feedback (RLHF), and ethical AI, we want to hear from you.
Why Join Us?
- Work with state-of-the-art infrastructure and cutting-edge datasets.
- Competitive compensation package and equity options.
- Flexible remote-first culture with quarterly in-person innovation sprints.
Responsibilities
- Research & Development: Lead the research and engineering efforts for advanced Generative AI architectures, focusing on scalability, efficiency, and multimodal capabilities.
- Model Optimization: Optimize large-scale transformer models for inference speed and reduced computational cost using techniques like quantization, pruning, and knowledge distillation.
- Agentic Systems: Design and implement autonomous AI agents capable of complex, multi-step reasoning and tool use in dynamic environments.
- Team Leadership: Mentor a team of junior data scientists and machine learning engineers, fostering a culture of innovation and continuous learning.
- Deployment: Collaborate with MLOps engineers to deploy robust, secure, and scalable models into production environments.
- Ethical AI: Ensure all models adhere to strict ethical guidelines regarding bias, safety, and transparency.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, or a related field, with a focus on Deep Learning or Natural Language Processing.
- Experience: 5+ years of experience in research or applied machine learning, with at least 2 years in a leadership or senior technical role.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX. Strong understanding of transformer architectures, attention mechanisms, and pre-training objectives.
- Tooling: Experience with distributed training frameworks (Ray, Horovod) and MLOps platforms (Kubernetes, MLflow, Docker).
- Problem Solving: Demonstrated ability to solve complex, open-ended research problems and translate them into practical solutions.
- Communication: Excellent written and verbal communication skills, with the ability to present complex technical concepts to diverse audiences.