Job Description
We are pioneering the technological landscape for the year 2026 and beyond. At Nexus Horizon Labs, we are building the next generation of generative intelligence that understands context, emotion, and nuance. We are seeking a visionary Senior AI Research Engineer to lead our core research division.
In this role, you won't just be maintaining existing models; you will architect the future of artificial intelligence. You will work on cutting-edge projects that push the boundaries of what is possible in Natural Language Processing (NLP), Computer Vision, and Reinforcement Learning. If you are passionate about ethical AI, scalable architecture, and solving complex problems, we want to hear from you.
Why Join Us?
- Work on the core technology stack that defines the 2026 tech era.
- Competitive compensation package with equity options.
- Flexible remote-first culture with quarterly in-person innovation summits.
- Access to state-of-the-art computing infrastructure and proprietary datasets.
Responsibilities
- Design, develop, and train state-of-the-art deep learning models to solve complex real-world problems.
- Research and implement novel algorithms in the domain of Large Language Models (LLMs) and multimodal AI.
- Collaborate with cross-functional teams of engineers, product managers, and ethicists to translate research into scalable products.
- Conduct rigorous experimentation, hyperparameter tuning, and model evaluation to ensure performance and accuracy.
- Mentor junior researchers and graduate students, fostering a culture of innovation and continuous learning.
- Stay abreast of the latest academic research and industry trends to ensure our technology remains ahead of the curve.
Qualifications
- Master’s or PhD degree in Computer Science, Machine Learning, Mathematics, or a related technical field.
- 5+ years of professional experience in AI/ML research or a significant track record in top-tier academic research.
- Strong proficiency in Python, PyTorch, or TensorFlow.
- Deep understanding of machine learning fundamentals, including neural networks, gradient descent, and optimization.
- Proven experience publishing research papers at top-tier conferences (NeurIPS, ICML, ICLR, ACL, etc.).
- Experience with cloud platforms (AWS, GCP, Azure) and MLOps pipelines.