Job Description
We are seeking a visionary Senior AI Research Engineer to help architect the next generation of intelligent systems. As we look toward the technological landscape of 2026, our mission is to redefine human-machine interaction through advanced generative models and autonomous agents.
In this role, you will lead the R&D efforts for our flagship AI platform, pushing the boundaries of Large Language Models (LLMs) and multimodal learning. You will work in a dynamic, high-performance environment where innovation is not just encouraged but required. If you are passionate about the future of AI and want to build the tools that will define the decade ahead, we want to meet you.
Why Join Us?
- Work on cutting-edge AI technology with a focus on the 2026 roadmap.
- Competitive equity and benefits package.
- Collaborate with world-class researchers and engineers.
Responsibilities
- Lead Research & Development: Design and implement novel algorithms to improve model performance, scalability, and energy efficiency for the 2026 AI ecosystem.
- Model Architecture: Architect and optimize deep neural networks, specifically focusing on Transformer models and reinforcement learning agents.
- Prototype Implementation: Translate theoretical research into production-ready code using Python, PyTorch, and TensorFlow.
- Mentorship: Guide junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Cross-Functional Collaboration: Partner with product managers and engineering teams to integrate AI capabilities into consumer-facing applications.
- Performance Evaluation: Conduct rigorous testing and benchmarking to ensure models meet safety and accuracy standards.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in AI/ML engineering, with at least 2 years focused on Generative AI.
- Technical Skills: Proficiency in Python, C++, and deep learning frameworks (PyTorch, TensorFlow, JAX).
- Knowledge: Strong understanding of NLP, computer vision, or multimodal learning architectures.
- Problem Solving: Proven ability to solve complex, unstructured problems in high-uncertainty environments.
- Communication: Excellent written and verbal communication skills, with the ability to present technical concepts to non-technical stakeholders.