Job Description
We are on the cusp of a technological revolution. FutureCore Systems is building the infrastructure for the next decade, and we need visionary minds to lead the charge for Project 2026. This is a rare opportunity to architect systems that will define the future of artificial intelligence, human-computer interaction, and global connectivity.
In this role, you will not just use existing frameworks; you will help build them. You will be responsible for designing the neural architectures and distributed systems that will power our next-generation AI solutions. If you are passionate about pushing the boundaries of what is possible and want to leave a lasting legacy in the tech world, we want to hear from you.
Why Join Us?
• Work on cutting-edge AI projects with a world-class team.
• Competitive compensation and equity package.
• Flexible remote and hybrid work options.
• Access to the latest hardware and research tools.
Responsibilities
- Design and implement scalable neural network architectures for Project 2026, focusing on large language models and autonomous agents.
- Lead the R&D strategy for next-generation AI infrastructure, ensuring high availability, security, and performance.
- Collaborate with cross-functional teams including researchers, product managers, and security experts to translate complex concepts into deployable code.
- Mentor junior engineers and provide technical guidance on best practices in machine learning and software engineering.
- Optimize model inference pipelines to reduce latency and cost while maintaining high accuracy.
- Stay ahead of industry trends and evaluate emerging technologies to integrate into our core stack.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related field.
- 10+ years of experience in software engineering, with at least 5 years specializing in AI/ML systems.
- Deep expertise in Python, PyTorch, TensorFlow, and distributed computing systems (Kubernetes, Docker).
- Proven experience deploying and scaling large-scale machine learning models in production environments.
- Strong understanding of neural network theory, natural language processing (NLP), and computer vision.
- Excellent problem-solving skills and the ability to thrive in a fast-paced, ambiguous environment.