Job Description
Are you ready to define the landscape of Artificial Intelligence in 2026?
Nexus Future Systems is at the forefront of the next industrial revolution. We are seeking a visionary Senior AI Research Engineer to lead our R&D division. In this pivotal role, you will architect the neural networks and algorithmic frameworks that will power the intelligent systems of tomorrow. This is not just a job; it is a mission to bridge the gap between theoretical AI and real-world application in the coming years.
We offer a competitive compensation package, equity opportunities, and a culture that champions radical innovation and ethical AI development.
Responsibilities
- Architect Next-Gen Models: Design and implement cutting-edge deep learning architectures tailored for 2026 computational requirements, focusing on efficiency and scalability.
- Research Leadership: Spearhead research initiatives in Generative AI and Large Language Models, publishing findings in top-tier academic conferences.
- Model Optimization: Reduce inference latency and memory footprint of large models using quantization and pruning techniques.
- Cross-Functional Collaboration: Work closely with product engineering teams to translate theoretical models into deployable, production-grade software.
- Ethical AI Governance: Establish guidelines and frameworks to ensure AI safety, fairness, and transparency in our systems.
- Talent Development: Mentor junior researchers and engineers, fostering a high-performance, collaborative team culture.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, or a related quantitative field (or equivalent professional experience).
- Experience: 5+ years of hands-on experience in machine learning, deep learning, or natural language processing.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and C++. Experience with MLOps tools (Kubeflow, MLflow).
- Domain Knowledge: Strong understanding of transformer models, reinforcement learning, or computer vision.
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems with novel algorithmic solutions.
- Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.