Job Description
Join the Frontier of Artificial Intelligence.
Nexus Future Systems is pioneering the technological breakthroughs required for the 2026 Horizon. We are seeking a visionary AI Research Engineer to architect the next generation of General Intelligence systems. You will work at the intersection of deep learning, quantum computing, and neural architecture search.
In this role, you won't just use existing tools—you will help define the standards for the future. You will lead critical initiatives in autonomous decision-making, ethical AI alignment, and scalable large-scale model training.
Why Join Us?
- Work on projects that define the roadmap for 2026 and beyond.
- Competitive equity package and performance bonuses.
- Access to cutting-edge compute infrastructure and research grants.
Key Objectives:
- Design and implement novel neural network architectures optimized for edge devices and cloud environments.
- Spearhead research into multimodal learning systems capable of real-time interaction.
- Collaborate with cross-functional teams including quantum physicists and data ethicists.
Responsibilities
- Architect Next-Gen Models: Design and train proprietary large language models and reinforcement learning agents focused on complex problem-solving.
- Optimize Inference: Reduce latency and computational cost for deployment on high-volume distributed systems.
- Publish Research: Author high-impact papers for top-tier conferences (NeurIPS, ICML) and open-source repositories.
- Prototype Protocols: Build and validate proof-of-concept systems for autonomous decision-making frameworks.
- Ethical Alignment: Implement safety guardrails and bias mitigation strategies in model training pipelines.
- Technical Leadership: Mentor junior researchers and set technical standards for the AI research lab.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Mathematics, or a related field with a focus on Artificial Intelligence.
- Experience: 5+ years of professional experience in machine learning research or applied AI engineering.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and experience with distributed training frameworks (Ray, Horovod).
- Specialization: Deep understanding of Transformer architectures, diffusion models, or reinforcement learning.
- Communication: Exceptional ability to communicate complex technical concepts to both technical and non-technical stakeholders.
- Adaptability: Experience working in agile, fast-paced environments with rapid iteration cycles.