“Get ready to push the boundaries of Artificial Intelligence! Expertshub.ai is looking for a talented Artificial Intelligence Researcher to join their team as a freelancer. You’ll have the opportunity to explore cutting-edge AI methodologies, develop innovative models, and bridge the gap between research and practical applications. If you’re passionate about AI and innovation, this role is for you!”
Quick Snapshot
Job Type: Full-time, Part-time
Salary: $60 – $75 per hour
Firm: Expertshub.ai
Location: Remote
Full job description
As a freelance Artificial Intelligence Researcher, you’ll explore and advance cutting-edge AI methodologies, bridging the gap between theoretical research and practical applications. This role is ideal for professionals passionate about innovation, experimentation, and developing novel AI techniques that push the boundaries of what’s possible.
Key Responsibilities
- Conduct applied and theoretical research in AI, machine learning, and deep learning.
- Develop innovative models and algorithms for real-world challenges (e.g., NLP, computer vision, generative AI, reinforcement learning).
- Analyze and publish findings, ensuring reproducibility and scalability of research outcomes.
- Collaborate with engineering teams to transition research concepts into deployable solutions.
- Stay current with academic and industry advancements in AI/ML.
- Contribute to intellectual property (papers, patents, prototypes)
Job Types: Full-time, Part-time
Pay: $60.00 – $75.00 per hour
Work Location: Remote
Requirements
- Advanced degree (Master’s/PhD) in Computer Science, AI, Machine Learning, or related fields, or equivalent research experience.
- Strong background in mathematics, statistics, and algorithm design.
- Hands-on experience with Python and AI frameworks (TensorFlow, PyTorch, Hugging Face, JAX).
- Research track record (publications, open-source contributions, patents) is a plus.
- Ability to experiment independently and communicate findings effectively.
Preferred Skills
- Experience with large-scale datasets and distributed training.
- Knowledge of transformer architectures, LLMs, GANs, reinforcement learning, and generative models.
- Familiarity with cloud computing and ML Ops environments.
- Strong problem-solving skills and curiosity-driven mindset.