Job Description
The Future of Intelligence Starts Here.
We are Synthetix Future Labs, a premier research institution pushing the boundaries of Artificial General Intelligence (AGI). We are seeking a visionary Senior AI Research Scientist to lead our 2026 roadmap initiatives. In this pivotal role, you will architect the next generation of neural architectures designed to solve complex, real-world problems that were previously thought to be impossible. If you are passionate about the intersection of deep learning, cognitive science, and scalable infrastructure, we want to hear from you.
Join us in defining the ethical and technical standards for the era of 2026 and beyond.
Responsibilities
- Architect Next-Gen Models: Design and implement cutting-edge deep learning architectures, focusing on Transformer-based models and reinforcement learning agents.
- 2026 Roadmap Execution: Lead the technical strategy for our 2026 product release, ensuring our AI systems achieve unprecedented levels of accuracy and efficiency.
- Research & Publication: Publish high-impact research papers in top-tier conferences (NeurIPS, ICML, ICLR) to establish thought leadership in the AI community.
- System Optimization: Optimize large-scale model inference and training pipelines to reduce latency and computational costs.
- Mentorship: Guide a team of junior researchers and engineers, fostering a culture of innovation and rigorous scientific inquiry.
- Collaboration: Partner with cross-functional teams including product managers, ethicists, and data engineers to translate research into deployable solutions.
Qualifications
- Education: Ph.D. or M.S. in Computer Science, Machine Learning, Mathematics, or a related quantitative field.
- Technical Expertise: Deep proficiency in Python, PyTorch, or TensorFlow, with extensive experience implementing state-of-the-art neural networks.
- Experience: Minimum 5+ years of experience in AI/ML research, preferably in NLP, Computer Vision, or Robotics.
- Algorithm Design: Proven track record of designing novel algorithms that solve complex optimization problems.
- Tools: Experience with MLOps tools (MLflow, Kubeflow) and cloud platforms (AWS, GCP, or Azure).
- Communication: Exceptional ability to communicate complex technical concepts to both technical and non-technical stakeholders.