AI Researcher
Overview :
We are looking for an AI Researcher to advance the state of the art in artificial intelligence and machine learning. The ideal candidate will design, develop, and evaluate new algorithms, models, and frameworks that push the boundaries of what's possible in AI - from foundational research to applied innovation in real-world systems.
Key Responsibilities :
- Conduct original research in areas such as machine learning, deep learning, NLP, computer vision, reinforcement learning, or generative AI.
- Design and implement novel algorithms, architectures, and models for AI-driven applications.
- Collaborate with data scientists, engineers, and product teams to transition research prototypes into production systems.
- Publish research results in top-tier conferences and journals (e.g., Neu rips, ICML, CVPR, ACL).
- Analyze and interpret experimental results; evaluate model performance and robustness.
- Explore and contribute to open-source AI frameworks and libraries.
- Stay current with the latest AI research, trends, and emerging technologies.
Required Skills & Qualifications :
Ph.D. or master's in computer science, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or a related field.Strong background in machine learning, deep learning, and statistical modeling.Proficiency in Python and deep learning frameworks like Torch, TensorFlow, or JAX.Experience with data processing pipelines (NumPy, Pandas, scikit-learn) and distributed training (e.g., Horwood, Ray, Deep Speed).Proven ability to design and execute experiments and evaluations systematically.Excellent written and verbal communication skills - including the ability to write technical papers and present research findings.Preferred / Nice-to-Have :
Publications in recognized AI / ML venues (Neu rips, ICML, ICLR, CVPR, ACL, EMNLP, etc.).Experience with large language models (LLMs), transformers, or foundation models.Familiarity with reinforcement learning, probabilistic modeling, or causal inference.Hands-on experience with ML Ops, cloud-based training environments, or GPU / TPU optimization.
Experience contributing to open-source AI projects.
Typical Tools & Technologies :
Languages : Python, C++, Julia
Frameworks : Py Torch, TensorFlow, Hugging Face Transformers, JAX
Libraries : NumPy, Pandas, scikit-learn, Matplotlib
Platforms : AWS, GCP, Azure, local HPC clusters
Version Control : Git, GitHub, GitLab
(ref : hirist.tech)