Job Description
Shape the Intelligence of Tomorrow
Are you ready to architect the next generation of artificial intelligence? We are seeking a visionary Senior AI & Machine Learning Engineer to lead our research and development efforts, specifically focusing on the roadmap leading into 2026 and beyond.
At Nebula Horizon, we are not just building software; we are engineering the future of human-computer interaction. You will work at the intersection of deep learning, generative AI, and scalable infrastructure to solve complex problems that define the decade ahead.
Why Join Us?
• Work with a world-class team pushing the boundaries of what is possible in 2026.
• Competitive compensation package including equity and performance bonuses.
• Top-tier benefits and a flexible remote-first culture.
Responsibilities
- Architect Future-Proof Solutions: Design and deploy scalable machine learning pipelines and neural network architectures designed to remain robust and cutting-edge through 2026 and beyond.
- Optimize Large Language Models: Fine-tune and optimize proprietary LLMs for specific industry verticals, improving inference speed and accuracy.
- Build MLOps Pipelines: Establish automated CI/CD pipelines for model training, testing, and deployment, ensuring seamless integration into production environments.
- Conduct Advanced Research: Stay ahead of the curve by researching emerging paradigms in AI, such as Multimodal learning and Autonomous Agents.
- Collaborate with Visionary Teams: Partner with product managers and software engineers to translate technical roadmap goals into high-impact product features.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Mathematics, Statistics, or a related quantitative field.
- Experience: 5+ years of professional experience in machine learning engineering, with a proven track record of shipping production-grade AI models.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX. Strong understanding of distributed systems and cloud computing (AWS/GCP).
- Domain Knowledge: Deep understanding of Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
- Problem Solving: Ability to tackle ambiguous problems with creative, data-driven solutions.