Job Description
Are you ready to define the technological landscape of 2026 and beyond? Nexus Future Systems is seeking a visionary Senior AI & Machine Learning Engineer to lead our next-generation research division. We are building the foundational models that will power the autonomous economies of tomorrow. You will work at the intersection of Generative AI, Reinforcement Learning, and Ethical AI frameworks.
In this role, you will not just implement existing algorithms; you will pioneer new architectures designed for scalability, efficiency, and human-AI symbiosis. Join a team of elite engineers and researchers dedicated to pushing the boundaries of what is possible in artificial intelligence.
Why Join Us?
- Work on projects that will shape the future of global industries.
- Competitive compensation package including stock options and remote flexibility.
- Access to state-of-the-art compute infrastructure and proprietary datasets.
Responsibilities
- Architect Scalable ML Pipelines: Design, implement, and optimize end-to-end machine learning pipelines capable of processing petabytes of real-time data with sub-millisecond latency.
- Research & Development: Conduct cutting-edge research in Large Language Models (LLMs), Computer Vision, and Multimodal AI systems to push the state of the art.
- Model Optimization: Fine-tune and distill large models to run efficiently on edge devices and consumer hardware without compromising accuracy.
- Ethical AI Implementation: Develop and integrate fairness, accountability, and transparency (FAT) metrics into model training loops.
- Collaboration: Partner with product teams and data scientists to translate complex research into deployable, user-friendly AI solutions.
- Technical Leadership: Mentor junior engineers and conduct code reviews to maintain high engineering standards across the team.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Mathematics, Statistics, or a related field with a focus on AI/ML.
- Experience: 5+ years of professional experience in machine learning, deep learning, or AI research, preferably in a fast-paced tech environment.
- Technical Proficiency: Strong proficiency in Python, PyTorch, TensorFlow, and modern deep learning frameworks.
- Domain Knowledge: Deep understanding of Transformer architectures, Reinforcement Learning (RLHF), and Natural Language Processing (NLP).
- Problem Solving: Demonstrated ability to tackle ambiguous problems and deliver innovative solutions under tight deadlines.
- Communication: Excellent verbal and written communication skills with the ability to present complex technical concepts to non-technical stakeholders.