Job Description
Shape the Future of Intelligence. Nexus Future Labs is pioneering the next generation of Artificial General Intelligence (AGI) designed for the 2026 era. We are looking for a visionary Senior AI Research Engineer to architect, train, and deploy multimodal foundation models that will redefine human-machine interaction.
In this role, you won't just use existing tools; you will push the boundaries of algorithmic efficiency, synthetic data generation, and ethical AI alignment. If you are passionate about the trajectory of technology and want to build the systems that will power the world in 2026, we want to hear from you.
What You Will Do:
- Architect Scalable Models: Lead the design and implementation of proprietary Large Language Models (LLMs) and diffusion systems optimized for the 2026 computing landscape.
- Optimize Inference: Develop advanced quantization and pruning techniques to reduce latency and energy consumption in real-time generative applications.
- Lead R&D: Conduct cutting-edge research in prompt engineering, chain-of-thought reasoning, and reinforcement learning from human feedback (RLHF).
- Collaborate Across Disciplines: Partner with product teams and creative directors to translate technical capabilities into tangible user experiences.
- Ethical AI Stewardship: Establish guardrails and safety protocols to ensure AI outputs remain bias-free and aligned with regulatory standards.
Responsibilities
- Design and train large-scale generative models (Transformers, Diffusion, GANs).
- Implement efficient data pipelines for synthetic data generation and curation.
- Collaborate with MLOps engineers to deploy models to production environments.
- Research and implement state-of-the-art techniques in semantic search and vector databases.
- Present technical findings to internal stakeholders and the scientific community.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, or a related field, with a focus on Artificial Intelligence.
- Experience: 5+ years of experience in machine learning research or software engineering with a focus on AI.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow. Deep understanding of NLP and Computer Vision architectures.
- Problem Solving: Proven track record of solving complex algorithmic problems and optimizing model performance.
- Communication: Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to non-technical audiences.