Job Description
We are building the operating system for the next era of technology. At Nexus Future Systems, we are defining the trajectory of Artificial General Intelligence (AGI) and next-generation autonomous agents for the 2026 landscape. We are seeking a visionary Senior AI/ML Engineer to lead our core research division.
In this role, you won't just be building models; you will be architecting the future of human-machine interaction. You will work on cutting-edge Generative AI, Reinforcement Learning, and Neural Architecture Search to solve complex, unsolved problems in reasoning and automation.
Why join us?
- Impact: Your work will define the standard for AI capabilities in the coming decade.
- Autonomy: Work in a high-trust environment with minimal bureaucracy.
- Equity: Competitive stake in the company's massive growth trajectory.
Join us in shaping the intelligence layer of the world's digital infrastructure.
Responsibilities
- Architect Next-Gen Models: Design and implement scalable Deep Learning architectures capable of handling multi-modal inputs and complex reasoning tasks for the 2026 era.
- Optimize Inference Pipelines: Reduce latency and improve throughput for large language models (LLMs) using techniques like quantization, pruning, and efficient attention mechanisms.
- Research & Development: Conduct rigorous research in Reinforcement Learning from Human Feedback (RLHF) and Chain-of-Thought prompting to enhance model safety and alignment.
- Deployment Strategy: Oversee the transition from research prototypes to production-grade, fault-tolerant AI services ensuring high availability and low cost.
- Mentorship: Lead a team of junior data scientists and ML engineers, fostering a culture of innovation and continuous learning.
Qualifications
- Expertise: 5+ years of professional experience in Machine Learning, Deep Learning, or AI Research with a strong portfolio of published papers or deployed products.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow. Deep understanding of Transformer architectures, BERT, GPT, and diffusion models.
- Mathematical Foundation: Solid grounding in Linear Algebra, Calculus, Probability, and Statistics.
- Experience: Experience deploying AI models at scale using cloud infrastructure (AWS, GCP, or Azure).
- Communication: Ability to explain complex technical concepts to non-technical stakeholders and leadership.
- Education: Master’s or PhD in Computer Science, Physics, or a related quantitative field is highly preferred.