Job Description
Join the Architects of the 2026 AI Paradigm.
Nexus Future Labs is at the forefront of defining the technological landscape for the next decade. We are seeking a visionary Senior AI Research Engineer to lead the development of the next generation of autonomous, multimodal Large Language Models (LLMs). In this role, you will bridge the gap between theoretical machine learning breakthroughs and scalable production systems, directly shaping the AI capabilities expected in 2026 and beyond.
As a key member of our Core AI Division, you will work with a world-class team of researchers and engineers to solve complex challenges in reasoning, safety, and efficiency. We offer a competitive compensation package, significant equity, and the freedom to experiment with cutting-edge architectures.
Responsibilities
- Model Architecture Design: Architect and implement next-generation Transformer architectures and generative models optimized for the 2026 enterprise landscape, focusing on reasoning capabilities and reduced inference costs.
- Training & Fine-tuning: Spearhead the training pipelines for large-scale language models, utilizing advanced techniques such as Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI.
- Performance Optimization: Optimize model inference latency and throughput through quantization, distillation, and distributed training strategies.
- Ethical AI Development: Lead initiatives to ensure AI safety, fairness, and robustness, implementing guardrails to prevent hallucinations and bias.
- Research Publication: Collaborate with academic institutions to publish breakthrough findings and maintain Nexus Future Labs' position as an industry thought leader.
- Code Review & Mentorship: Mentor junior engineers and researchers, conducting rigorous code reviews to maintain high engineering standards across the AI team.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Machine Learning, Statistics, or a related quantitative field.
- Experience: 5+ years of professional experience in deep learning, Natural Language Processing (NLP), or machine learning research.
- Technical Proficiency: Strong proficiency in Python, PyTorch, TensorFlow, or JAX. Deep understanding of transformer models (BERT, GPT, LLaMA, etc.).
- System Design: Proven track record of designing and deploying large-scale ML systems on cloud infrastructure (AWS, GCP, or Azure).
- Programming: Advanced knowledge of C++ for high-performance computing and CUDA programming is a plus.
- Communication: Exceptional ability to communicate complex technical concepts to both technical and non-technical stakeholders.