Job Description
We are at the precipice of a technological revolution. As we look toward the year 2026, the boundaries between the physical and digital worlds will dissolve. OmniFuture Labs is seeking a visionary Senior AI Research Engineer to architect the next generation of Generative AI and Autonomous Agents.
In this role, you won't just maintain existing systems; you will pioneer the algorithms that define the future of human-computer interaction. You will work in a high-performance environment, leveraging cutting-edge hardware to solve complex problems that currently seem impossible. If you are obsessed with the future of AI and want to build the infrastructure that powers the world in 2026, we want to hear from you.
Why join us?
• Work with state-of-the-art AI infrastructure.
• Competitive equity and benefits.
• Focus on long-term impact and ethical AI development.
Responsibilities
- Design and implement scalable deep learning architectures for Large Language Models (LLMs) and multimodal systems targeting 2026 standards.
- Lead research initiatives in reinforcement learning and self-supervised learning to improve agent autonomy.
- Optimize model inference performance on next-gen GPU clusters and quantum-ready hardware.
- Collaborate with product teams to translate complex AI capabilities into user-friendly applications.
- Establish best practices for AI safety, fairness, and interpretability within the engineering team.
- Mentor junior researchers and engineers, fostering a culture of innovation and continuous learning.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of professional experience in AI/ML research or production-level ML engineering.
- Extensive experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Strong proficiency in Python and C++ for high-performance computing.
- Demonstrated expertise in NLP, Computer Vision, or Generative Adversarial Networks (GANs).
- Experience with MLOps pipelines, cloud infrastructure (AWS/GCP), and model versioning.