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AI Research Scientist
1 month ago
Fractal is a leading provider of Artificial Intelligence to Fortune 500 companies, aiming to power every human decision in the enterprise and bring AI, engineering, and design to help the world's most admired companies.
The company has 4000+ employees across 16 global locations, including the United States, UK, Ukraine, India, Singapore, and Australia. Fractal has been recognized as 'Great Workplace' and 'India's Best Workplaces for Women' in the top 100 (large) category by The Great Place to Work Institute.
About the RoleWe are seeking a talented AI Research Scientist to join our team and contribute to the development of our AI-driven products and services. The successful candidate will be responsible for conducting research and staying up-to-date with the latest advancements in Machine vision, NLP, Deep Learning, RL, Agentic systems, and related fields.
The ideal candidate will have a strong understanding of advanced DL or generative architectures/techniques, such as diffusion architectures, attention models, LLMs, SAM, RLHF, etc. They will also have a track record of coming up with new ideas or improving upon existing ideas in deep learning quickly, demonstrated by accomplishments such as first-author publications or published/deployed projects or impactful blogs.
Responsibilities- Conduct research and stay up-to-date with the latest advancements in Machine vision, NLP, Deep Learning, RL, Agentic systems, and related fields.
- Design and develop novel and next-generation AI algorithms, models, and techniques to address specific problem domains.
- Build AI Engineering pipelines for scaling AI models to internet scale.
- Document research findings, methodologies, and experimental results in technical reports, blogs, and papers.
- Publish research findings and contribute to conferences, workshops, and other scientific forums.
- Collaborate with engineering and business teams on model deployment and customized training respectively.
- Extensive understanding of advanced DL or generative architectures/techniques like diffusion architectures, attention models, LLMs, SAM, RLHF, etc.
- Have a track record of coming up with new ideas or improving upon existing ideas in deep learning quickly, demonstrated by accomplishments such as first-author publications or published/deployed projects or impactful blogs.
- Ability to efficiently work on a modern deep learning stack, such as Python, PyTorch, and GPU-enabled compute, eg writing non-trivial custom architecture in Pytorch.
- Ability to iterate quickly on open-source code-bases with attention to backwards compatibility, usability, and readability.
- Creative, fast-paced executor, detail-oriented, eager to learn, acquire new skills.
- Ability to work effectively in a collaborative team environment and take ownership.
- Strong belief in a positive-sum game, valuing cooperation and collaboration over zero-sum competition.
