Applied AI Research Engineer

Accountabilities:

• Build reinforcement learning and agent environments for real-world AI use cases, including defining task specifications, scoring mechanisms, evaluation criteria, and relevant testing workflows.
• Develop benchmarks and evaluation harnesses to assess model and data quality across dimensions such as accuracy, robustness, safety, latency, and cost.
• Design and implement LLM pipelines and agentic systems that support research initiatives, model evaluation, experimentation, and customer trials.
• Conduct fine-tuning, adapter, and other model experiments to understand how different datasets, techniques, and configurations influence model behavior and system performance.
• Deploy local and self-hosted models for evaluation, inference, experimentation, and automation workflows.
• Document experiments, configurations, datasets, results, methodologies, and known limitations clearly so that other engineers can reproduce, validate, and extend the work.
• Collaborate closely with AI research teams and cross-functional stakeholders to translate technical concepts into practical, reusable solutions and assets.
• Independently investigate technical problems, rapidly prototype potential approaches, and turn research questions or ideas into functional, production-oriented implementations.
• Contribute to the development of reliable, maintainable AI systems while applying strong engineering practices throughout experimentation and deployment.

Requirements:

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or a related technical discipline.
• At least 3 years of professional engineering or relevant industry experience in AI/ML, software engineering, or a closely related field.
• Strong software engineering capabilities with demonstrated experience building reliable, maintainable, and reusable AI or software systems.
• Hands-on experience developing agentic systems, reinforcement learning environments, LLM pipelines, or comparable AI applications.
• Proven experience creating evaluation harnesses, benchmarks, model-testing pipelines, or other systematic approaches to measuring AI system performance.
• Strong understanding of experimentation, reproducibility, evaluation methodologies, and technical documentation.
• Ability to work independently on complex technical problems, exercise sound engineering judgment, and move efficiently from an idea or research question to a working solution.
• Strong analytical and problem-solving skills, with curiosity and enthusiasm for experimenting with emerging AI techniques and technologies.
• Experience with synthetic data generation systems or dataset development is a plus.
• Published research papers, benchmarks, or other technical research is advantageous.
• Experience with SWE-bench or comparable software engineering evaluation environments is desirable.
• Experience building or deploying local inference systems, open-weight models, or self-hosted model environments is a plus.

Benefits:

• Permanent, regular full-time position with a remote working arrangement in India.
• High degree of autonomy and ownership over applied AI research and engineering projects.
• Opportunity to work on advanced AI challenges spanning reinforcement learning, agentic systems, LLMs, model evaluation, and AI experimentation.
• Exposure to practical research applications supporting both advanced AI initiatives and real-world customer use cases.
• Opportunity to collaborate closely with AI research and cross-functional technical teams.
• Environment that values curiosity, accountability, innovation, collaboration, and continuous learning.
• Opportunity to develop reusable AI assets, evaluation frameworks, and systems that can influence future AI applications.
• Flexibility to work effectively in a remote environment while accessing tools, resources, and development opportunities to strengthen technical capabilities.