Job Description
Be the architect of the next generation of intelligence. At FutureCore Systems, we are not just predicting the future; we are coding it. We are seeking a visionary Senior AI Research Engineer to lead our cutting-edge initiatives in Generative AI, Neural Architecture Search, and Autonomous Systems for the 2026 era.
We are looking for a pioneer who thrives in ambiguity and is obsessed with pushing the boundaries of what is possible with Large Language Models and Reinforcement Learning. If you want to build the infrastructure that powers the digital world of tomorrow, apply today.
Why Join Us?
- Work on high-impact projects that shape the future of technology.
- Competitive compensation and equity package.
- Flexible remote-first culture with state-of-the-art equipment.
Key Responsibilities:
- Design and implement novel neural network architectures optimized for 2026-scale data processing.
- Lead research initiatives in Large Language Models (LLMs) and multimodal AI systems.
- Optimize model inference speeds for edge devices and cloud environments.
- Mentor junior researchers and data scientists, fostering a culture of innovation.
- Collaborate with cross-functional teams to translate theoretical research into production-ready products.
- Stay ahead of the curve by publishing papers in top-tier AI conferences (NeurIPS, ICML, ICLR).
Qualifications:
- PhD or Master's degree in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of experience in research engineering, with a proven track record of publishing.
- Expert proficiency in Python, PyTorch, or TensorFlow.
- Deep understanding of Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
- Strong experience with distributed computing systems (Ray, Kubernetes) and cloud platforms (AWS, GCP).
- Demonstrated ability to tackle complex problems with creative algorithmic solutions.
Responsibilities
- Design and implement novel neural network architectures optimized for 2026-scale data processing.
- Lead research initiatives in Large Language Models (LLMs) and multimodal AI systems.
- Optimize model inference speeds for edge devices and cloud environments.
- Mentor junior researchers and data scientists, fostering a culture of innovation.
- Collaborate with cross-functional teams to translate theoretical research into production-ready products.
- Stay ahead of the curve by publishing papers in top-tier AI conferences (NeurIPS, ICML, ICLR).
Qualifications
- PhD or Master's degree in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of experience in research engineering, with a proven track record of publishing.
- Expert proficiency in Python, PyTorch, or TensorFlow.
- Deep understanding of Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
- Strong experience with distributed computing systems (Ray, Kubernetes) and cloud platforms (AWS, GCP).
- Demonstrated ability to tackle complex problems with creative algorithmic solutions.