Job Description
The Opportunity: At Nexus Horizon Labs, we are not just predicting the future; we are architecting it. As we look toward the technological milestones of 2026, we are seeking a visionary Senior AI Engineer to lead our generative AI and machine learning initiatives.
You will work at the intersection of deep learning and scalable infrastructure, building the systems that will power the next decade of human-computer interaction. If you are passionate about pushing the boundaries of what is possible in artificial intelligence, this is your chance to define the future.
Why Join Us?
- Work with cutting-edge technologies in a fully remote-first, high-performance environment.
- Competitive equity package and comprehensive benefits.
- Focus on high-impact projects with clear pathways to leadership.
Responsibilities
- Architecting Intelligence: Design, develop, and deploy advanced machine learning models, focusing on Large Language Models (LLMs) and Generative AI applications.
- System Optimization: Lead the architecture for proprietary AI infrastructure, ensuring high performance, low latency, and maximum scalability.
- Productionization: Translate complex research concepts into robust, production-ready code using MLOps best practices.
- Cross-Functional Collaboration: Partner with product managers, designers, and engineers to integrate AI capabilities seamlessly into core product ecosystems.
- Mentorship: Mentor junior data scientists and engineers, fostering a culture of continuous learning and technical excellence.
- Innovation: Stay ahead of industry trends to implement novel algorithms and methodologies that give us a competitive edge.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related field (or equivalent professional experience).
- Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or NLP.
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with Hugging Face and LangChain.
- Infrastructure: Deep understanding of cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Problem Solving: Proven track record of solving complex mathematical and engineering challenges.
- Communication: Exceptional ability to communicate technical concepts to non-technical stakeholders and write clear documentation.