Job Description
Lead deep-tech research at the intersection of AI, systems engineering, and domain science.
Key Responsibilities
- Conduct original research in cutting-edge areas of AI, systems engineering, and domain-specific technologies.
- Design, prototype, and validate novel algorithms to solve real-world challenges.
- Publish high-impact research papers in reputed conferences and journals (e.g., NeurIPS, ICML, CVPR).
- Collaborate cross-functionally with engineering, product, and data science teams to integrate research outcomes into production systems.
- Evaluate and benchmark algorithm performance on large-scale datasets and real-world applications.
- Stay up to date with the latest developments in AI, machine learning, and software systems.
- Participate in grant proposals, patent filings, and other knowledge dissemination activities.
- Contribute to open-source initiatives or internal knowledge bases to foster a research-driven culture.
- Present findings in internal reviews, workshops, and industry forums to build thought leadership.
Qualification
- PhD or Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field.
- Solid academic background with a strong foundation in algorithms, statistics, and systems.
- Demonstrated experience in machine learning and deep learning, including hands-on model development and evaluation.
- Track record of publications in top-tier AI/ML conferences or journals is highly desirable.
- Familiarity with research methodologies, experimental design, and reproducibility practices.
- Experience working on real-world AI applications or industry research labs is a plus.
- Strong coding skills in Python and frameworks like TensorFlow, PyTorch, or JAX.
- Ability to independently drive research initiatives from ideation to implementation.
Skills
Core Technical Skills
- Machine Learning (ML)
- Deep Learning (DL)
- Reinforcement Learning (RL)
- Natural Language Processing (NLP)
- Computer Vision
- Signal Processing
- Probabilistic Modeling
- Optimization Algorithms
- Distributed Systems
- High-Performance Computing (HPC)
Programming & Tools
- Python (NumPy, pandas, scikit-learn)
- Deep Learning Frameworks: TensorFlow, PyTorch, JAX
- C/C++, Java (for performance-critical modules)
- Git, Docker, Kubernetes
- MLFlow or Weights & Biases (for experiment tracking)
- Linux, Bash scripting
- MATLAB or R (for statistical computing, if applicable)
