Job Description
Architect the Intelligence of Tomorrow.
Nexus Horizon Labs is seeking a visionary Lead AI & Autonomy Engineer to define the technological landscape of 2026. We are building the autonomous systems and generative models that will redefine human-machine interaction.
In this role, you will move beyond traditional software development to pioneer the next generation of Artificial General Intelligence (AGI) components. You will lead a high-performance team in designing, training, and deploying scalable AI models that operate with zero-latency efficiency and high ethical standards.
Why Join Us?
We offer a competitive compensation package, equity opportunities, and the chance to work on projects that matter. If you are passionate about the future of technology and possess the expertise to build it, we want to hear from you.
Responsibilities
- Model Architecture: Design and implement state-of-the-art generative AI models (LLMs) optimized for edge deployment and real-time inference.
- Autonomy Systems: Develop reinforcement learning algorithms that enable autonomous decision-making in complex, dynamic environments.
- Infrastructure: Build and maintain high-throughput, distributed training pipelines using GPU clusters and cloud infrastructure.
- AI Governance: Establish rigorous frameworks for AI safety, bias mitigation, and ethical compliance.
- Collaboration: Partner with product managers and engineers to integrate AI capabilities seamlessly into consumer and enterprise products.
- Research: Stay at the forefront of AI research to prototype novel techniques in prompt engineering and semantic understanding.
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 AI/ML engineering, with a focus on Large Language Models (LLMs) and Transformers.
- Programming: Expert proficiency in Python, PyTorch, or TensorFlow, with strong knowledge of C++ for performance optimization.
- NLP: Deep understanding of Natural Language Processing (NLP), tokenization strategies, and semantic analysis.
- Cloud: Experience deploying models to AWS, Google Cloud, or Azure using containerization (Docker/Kubernetes).
- Problem Solving: Demonstrated ability to solve complex mathematical and algorithmic challenges at scale.