Research Engineer - Code Generation & Model Evaluatio

Company Description N.R. Solutions Ltd. is a technology-focused company dedicated to developing advanced software and AI-driven solutions for complex real-world problems. The organization emphasizes innovation, rigorous experimentation, and practical application of cutting-edge research. Team members collaborate across disciplines to design, build, and refine systems that improve efficiency and decision-making for clients and internal products. The work culture supports remote collaboration, continuous learning, and ownership of technical outcomes. Candidates joining the team can expect an environment that values curiosity, transparency, and measurable impact.
Role Description The Research Engineer – Code Generation & Model Evaluation will work full time in a remote capacity, focusing on designing, implementing, and analyzing systems for automated code generation and evaluation of machine learning models. Day-to-day responsibilities include developing and testing algorithms, building tools and pipelines that generate and assess code snippets, and running experiments to compare model performance across tasks and datasets. The role involves designing evaluation protocols, writing research-grade reports, and contributing to technical documentation that informs product and research decisions. The Research Engineer will collaborate with other engineers and researchers to refine model architectures, improve reliability and efficiency of code generation workflows, and translate research findings into practical improvements for production systems. The position also requires maintaining clean, reproducible codebases and actively participating in remote meetings, code reviews, and collaborative planning sessions.
Qualifications
Strong foundation in Computer Science, including data structures, algorithms, and software engineering principles.
Experience in Research and Development (R&D) and applied Research, with the ability to design experiments, analyze results, and communicate findings clearly.
Background in Physics or related quantitative fields, with proven analytical and problem-solving skills.
Proficiency in algorithm design and optimization, especially in contexts related to code generation, model evaluation, or machine learning.
Hands-on experience with programming languages such as Python and familiarity with frameworks used in machine learning and model evaluation.
Ability to work effectively in a fully remote environment, including strong written communication, documentation habits, and collaborative skills.
Comfort with version control tools (e.g., Git) and reproducible research practices.
Bachelor’s, Master’s, or PhD in Computer Science, Physics, Engineering, or a related technical field, or equivalent practical experience.
Experience with AI coding assistants, large language models, or automated code analysis tools is a plus.

Work arrangement
Yes