AI Researcher

Role overview

Research and model development

• Research AI, machine learning, deep learning, and emerging technologies; design, develop, and evaluate models, algorithms, and experimental methods.
• Explore NLP, computer vision, generative AI, reinforcement learning, and LLM techniques as project needs require.
• Review academic, technical, open-source, and industry research; formulate hypotheses and design experiments.
• Build prototypes and proof-of-concept implementations to validate findings.
• Collect, preprocess, and analyze datasets; train, fine-tune, benchmark, and optimize models.
• Assess accuracy, robustness, efficiency, scalability, and generalization, and investigate performance and resource-use improvements.

Collaboration and responsible research

• Work with engineering, data science, product, and business teams to apply research to business challenges, automation, decision support, and intelligent products.
• Maintain reproducible experiments and documentation; communicate findings through technical reports and presentations.
• Evaluate AI limitations, reliability, fairness, explainability, and risks; apply responsible AI principles, privacy practices, and safeguards.
• Contribute to research roadmaps, technical strategy, innovation initiatives, publications, intellectual property, collaborations, and improved research workflows.
• Stay current with AI architectures, training methods, evaluation frameworks, and infrastructure; present recommendations to teams, management, and stakeholders.

Requirements

• Proven experience in AI research, machine learning, deep learning, data science, or a related technical field.
• Bachelor’s or Master’s degree in computer science, AI, machine learning, mathematics, statistics, electrical engineering, or a related discipline.
• A PhD or equivalent research experience is advantageous for research-intensive positions.
• Strong Python programming and familiarity with relevant AI and scientific-computing libraries.
• Practical experience with PyTorch, TensorFlow, or JAX; strong understanding of machine-learning algorithms, statistics, optimization, and model evaluation.
• Experience designing experiments, analyzing results, and validating hypotheses; familiarity with deep-learning architectures and neural networks.
• Knowledge of data preprocessing, feature engineering, model training, and performance optimization.
• NLP, computer vision, generative AI, LLM, or other relevant specialization experience is advantageous.
• Strong foundations in probability, statistics, linear algebra, and calculus; familiarity with version control and reproducible research.
• Problem-solving ability, intellectual curiosity, technical writing, documentation, communication, presentation, and effective independent and team collaboration.
• Research publications, open-source contributions, technical projects, or a strong research portfolio are highly advantageous.

What the employer offers

• Competitive compensation and a comprehensive benefits package.
• Innovative AI research, challenging problems, experimentation, and collaboration with researchers, engineers, data scientists, and product teams.
• Professional development, technical training, and opportunities to attend conferences and research events.
• Opportunities to contribute to real-world AI applications, publications, open-source projects, and intellectual property.
• Potential career progression to Senior AI Researcher, Research Scientist, AI Research Lead, Principal Research Scientist, or Head of AI Research.