Job Description
Are you ready to define the trajectory of artificial intelligence? Future Systems Inc. is seeking a visionary Senior AI Research Engineer to join Project 2026, our groundbreaking initiative to engineer scalable, autonomous intelligence for the post-silicon era.
In this role, you will not just be writing code; you will be architecting the neural foundations of tomorrow. We are looking for a self-starter who thrives in ambiguity and possesses an insatiable curiosity for pushing the boundaries of Generative AI, Large Language Models (LLMs), and quantum-ready algorithms.
Why join Project 2026?
• Impact at Scale: Your work will directly influence the infrastructure powering the next decade of digital transformation.
• Cutting-Edge Tech: Work with proprietary hardware accelerators and state-of-the-art frameworks.
• Global Leadership: Collaborate with world-class researchers from top-tier institutions.
Responsibilities
- Design and implement novel neural network architectures optimized for the Project 2026 infrastructure stack.
- Lead the end-to-end lifecycle of AI model development, from data curation and preprocessing to fine-tuning and deployment.
- Collaborate cross-functionally with hardware engineers to optimize inference latency on next-gen processors.
- Conduct rigorous empirical research to validate theoretical models and publish findings in top-tier conferences.
- Establish best practices for MLOps, ensuring reproducibility and scalability of our models.
- Provide technical mentorship to junior engineers and research scientists on the team.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Mathematics, or a related field, with a focus on Machine Learning or Artificial Intelligence.
- Minimum of 5+ years of professional experience in building production-grade AI systems.
- Deep expertise in Python, PyTorch, or TensorFlow, with a proven track record of publishing open-source contributions.
- Strong understanding of Deep Learning principles, specifically in NLP, Computer Vision, or Reinforcement Learning.
- Experience with distributed training systems (e.g., Ray, Spark) and cloud-based MLOps platforms (AWS, GCP, Azure).
- Exceptional problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.