hybrid

AI Research Scientist

The AI Research Scientist will design, execute, and analyze machine learning experiments, staying updated with the latest AI research to adapt novel techniques for company use cases. This role involves collaborating with cross-functional teams to refine problem statements, develop reusable research assets, and optimize ML pipelines for production integration.

About the role

Responsibilities

Design, execute, and analyze machine learning experiments, establishing strong baselines and selecting appropriate evaluation metrics.
Stay up to date with the latest AI research; identify, adapt, and validate novel techniques for company-specific use cases.
Define rigorous evaluation protocols, including offline metrics, user studies, and adversarial (red team) testing to ensure statistical soundness.
Specify data and annotation requirements; develop annotation guidelines and oversee quality control processes.
Collaborate closely with domain experts, product managers, and engineering teams to refine problem statements and operational constraints.
Develop reusable research assets such as datasets, modular code components, evaluation suites, and comprehensive documentation.
Work alongside ML Engineers to optimize training and inference pipelines, ensuring seamless integration into production systems.
Contribute to academic publications and represent the company in research communities, as needed.

Educational Qualifications

Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field is strongly preferred.
Candidates with a master’s degree and exceptional research or industry experience will also be considered.

Industry Experience

3–5 years of experience in AI/ML research roles, ideally in applied or product-focused environments.
Demonstrated success in delivering research-driven solutions that have been deployed in production.
Experience collaborating in cross-functional teams across research, engineering, and product.
Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ACL, CVPR) are a plus.

Technical Skills

Strong foundational knowledge in machine learning and deep learning algorithms.
Hands-on experience with PEFT/LoRA, adapters, fine-tuning techniques, and RLHF/RLAIF (e.g., PPO, DPO, GRPO).
Ability to read, implement, and adapt state-of-the-art research papers to real-world use cases.
Proficiency in hypothesis-driven experimentation, ablation studies, and statistically sound evaluations.
Advanced programming skills in Python (preferred), C++, or Java.
Experience with deep learning frameworks such as PyTorch, Hugging Face, NumPy, etc.
Strong mathematical foundations in probability, linear algebra, and calculus.
Domain expertise in one or more areas: natural language processing (NLP), symbolic reasoning, speech processing, etc.
Ability to translate research insights into roadmaps, technical specifications, and product improvements.

About the role

Responsibilities

Design, execute, and analyze machine learning experiments, establishing strong baselines and selecting appropriate evaluation metrics.
Stay up to date with the latest AI research; identify, adapt, and validate novel techniques for company-specific use cases.
Define rigorous evaluation protocols, including offline metrics, user studies, and adversarial (red team) testing to ensure statistical soundness.
Specify data and annotation requirements; develop annotation guidelines and oversee quality control processes.
Collaborate closely with domain experts, product managers, and engineering teams to refine problem statements and operational constraints.
Develop reusable research assets such as datasets, modular code components, evaluation suites, and comprehensive documentation.
Work alongside ML Engineers to optimize training and inference pipelines, ensuring seamless integration into production systems.
Contribute to academic publications and represent the company in research communities, as needed.

Educational Qualifications

Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field is strongly preferred.
Candidates with a master’s degree and exceptional research or industry experience will also be considered.

Industry Experience

3–5 years of experience in AI/ML research roles, ideally in applied or product-focused environments.
Demonstrated success in delivering research-driven solutions that have been deployed in production.
Experience collaborating in cross-functional teams across research, engineering, and product.
Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ACL, CVPR) are a plus.

Technical Skills

Strong foundational knowledge in machine learning and deep learning algorithms.
Hands-on experience with PEFT/LoRA, adapters, fine-tuning techniques, and RLHF/RLAIF (e.g., PPO, DPO, GRPO).
Ability to read, implement, and adapt state-of-the-art research papers to real-world use cases.
Proficiency in hypothesis-driven experimentation, ablation studies, and statistically sound evaluations.
Advanced programming skills in Python (preferred), C++, or Java.
Experience with deep learning frameworks such as PyTorch, Hugging Face, NumPy, etc.
Strong mathematical foundations in probability, linear algebra, and calculus.
Domain expertise in one or more areas: natural language processing (NLP), symbolic reasoning, speech processing, etc.
Ability to translate research insights into roadmaps, technical specifications, and product improvements.