Job Description:
We are seeking a highly motivated and innovative AI Research Scientist to join our cutting-edge AI research team. In this role, you will design, develop, and implement state-of-the-art machine learning and deep learning algorithms that solve real-world problems and push the boundaries of AI. You will contribute to both applied and theoretical advancements in AI, collaborate with cross-functional teams, and publish in top-tier conferences and journals.
This position requires a deep understanding of artificial intelligence, strong mathematical foundations, hands-on experience with large-scale models, and a passion for experimentation and innovation.
Responsibilities:
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Conduct original research in machine learning, deep learning, NLP, computer vision, or reinforcement learning.
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Design and prototype novel AI algorithms and models to solve real-world problems with high impact.
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Translate research insights into scalable production-ready models in collaboration with engineering teams.
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Publish high-quality research in top AI conferences (e.g., NeurIPS, ICML, CVPR, ACL, ICLR) and journals.
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Collaborate with product teams to identify AI opportunities, define project scopes, and evaluate outcomes.
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Benchmark and evaluate the performance and fairness of AI models against SOTA techniques.
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Develop technical documentation, present findings to internal and external stakeholders.
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Stay updated with emerging trends in AI and explore how they can be applied within the company.
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Contribute to open-source projects or proprietary internal tooling, when relevant.
Preferred Qualifications:
Required
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Ph.D. in Computer Science, Machine Learning, Mathematics, Statistics, or a related field; or a Master’s with equivalent research or industry experience.
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3+ years of experience conducting AI/ML research in academia or industry.
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Strong expertise in one or more of the following areas:
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Deep Learning (CNNs, RNNs, Transformers)
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Natural Language Processing
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Computer Vision
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Reinforcement Learning
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Generative Models (e.g., GANs, VAEs, diffusion models)
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Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX).
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Strong background in mathematics, especially linear algebra, probability, statistics, and optimization.
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Proven ability to publish in peer-reviewed venues or demonstrate innovative applied research.
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Excellent problem-solving and critical thinking skills.
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Strong communication skills; ability to explain technical topics to non-technical stakeholders.
Preferred
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Experience deploying models into production environments (e.g., via cloud services, APIs, or containerization).
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Familiarity with distributed computing or large-scale model training (e.g., GPUs, TPUs).
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Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Weights & Biases).
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Experience working with foundation models or large language models (e.g., BERT, GPT, CLIP).
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Contributions to open-source ML libraries or participation in research communities.
