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.

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