Job Description
We are on the cusp of the next industrial revolution. At Future Systems Inc., we aren't just building the future; we are defining it. We are looking for a visionary Senior AI Research Engineer to lead our core research division focused on next-generation Generative AI and Autonomous Systems.
In this role, you will bridge the gap between theoretical research and scalable production systems. You will work alongside world-class physicists, cryptographers, and software engineers to build the foundational models that will power the digital world of 2026 and beyond.
Why join us?
- Impactful Work: Directly influence the trajectory of AGI development.
- Top-Tier Compensation: Competitive salary, equity package, and full benefits.
- Flexible Environment: Remote-first culture with quarterly global meetups.
If you are obsessed with pushing the boundaries of what's possible with Large Language Models (LLMs) and Multimodal AI, we want to hear from you.
Responsibilities
- Research & Development: Design, implement, and evaluate state-of-the-art deep learning models, specifically focusing on Transformer architectures and reinforcement learning.
- Model Optimization: Optimize large-scale model inference for speed and accuracy, reducing latency in real-time applications.
- Publication: Author high-impact research papers for top-tier conferences (NeurIPS, ICML, ICLR) and contribute to open-source communities.
- Prototyping: Rapidly prototype novel AI concepts into production-ready Minimum Viable Products (MVPs).
- Mentorship: Lead and mentor a team of junior researchers and data scientists, fostering a culture of innovation and continuous learning.
- Collaboration: Partner with product teams to translate complex research findings into user-centric features.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, Physics, or a related field with a strong focus on AI/ML.
- Experience: 5+ years of experience in research or applied machine learning roles.
- Technical Skills: Deep proficiency in Python, PyTorch, or TensorFlow.
- Knowledge: Strong understanding of NLP, Computer Vision, or Reinforcement Learning.
- Problem Solving: Demonstrated ability to solve complex, unstructured problems with novel algorithmic approaches.
- Communication: Excellent written and verbal communication skills for technical documentation and presentations.