Design and develop reinforcement learning environments and agentic workflows to improve frontier AI model capabilities. Build infrastructure for LLM evaluation, benchmarking, and high-quality data pipeline development.
Requires at least 1 year of experience in software engineering, machine learning, or AI research with strong Python programming skills. Candidates should have a solid understanding of machine learning fundamentals and experience with LLM evaluation or agentic systems.
We are looking for a highly motivatedย AI / Machine Learning Engineerย to work on advanced AI initiatives focused on improving the capabilities, reliability, and performance of frontier AI models. The role involves building and evaluating sophisticated AI systems across areas such asย reinforcement learning, coding environments, agentic workflows, model evaluations, and high-quality enterprise data.
The ideal candidate will be excited by rapidly evolving AI technologies and comfortable working on complex, open-ended problems where experimentation, technical depth, and strong problem-solving skills are essential.
Key Responsibilities
Design and developย reinforcement learning environmentsย for coding, reasoning, and agentic AI applications.
Build tools, workflows, and infrastructure to evaluate the capabilities and limitations of advanced AI models.
Develop and maintainย LLM evaluation frameworks, benchmarks, and test environments.
Work on coding agents and agentic systems involving multi-step reasoning, tool use, planning, and task execution.
Develop high-quality datasets and enterprise data pipelines for AI training, evaluation, and model improvement.
Analyse model outputs and identify failure modes, behavioural patterns, and opportunities for improvement.
Design experiments to measure model performance across accuracy, reasoning, reliability, safety, and task completion.
Build prototypes and proof-of-concepts using modern AI and machine learning techniques.
Develop Python-based tools, services, and automation to support AI research and engineering workflows.
Collaborate with AI researchers, software engineers, data specialists, and product teams on complex technical problems.
Translate research ideas and experimental findings into scalable engineering solutions.
Improve the quality, reliability, and efficiency of AI evaluation and data-generation processes.
Contribute to technical documentation, experiment design, analysis, and knowledge sharing.
Stay current with developments inย LLMs, reinforcement learning, AI agents, model evaluation, and generative AI.
What Makes You a Great Fit
1+ years of experienceย in software engineering, machine learning, AI research, data science, or a related technical field.
Strong programming skills inย Pythonย and the ability to build reliable, maintainable software.
Strong interest inย Generative AI, LLMs, AI agents, and frontier model development.
Understanding of machine learning fundamentals, model training, evaluation, and experimentation.
Familiarity withย reinforcement learningย concepts and environments is highly desirable.
Experience with LLM evaluation, benchmarking, prompt engineering, or AI application development is an advantage.
Exposure toย agentic AI systems, tool calling, multi-step workflows, or autonomous task execution is desirable.
Strong analytical and problem-solving skills with the ability to investigate complex model behaviours.
Ability to work with structured and unstructured datasets and develop data-processing workflows.
Experience with APIs, Git, databases, cloud platforms, or modern software development practices is a plus.
Strong curiosity and willingness to work with rapidly evolving AI technologies.
Comfortable working on ambiguous problems and iterating quickly through experimentation.
Excellent communication and collaboration skills with the ability to work effectively across research and engineering teams.
A Bachelor's or Master's degree inย Computer Science, Artificial Intelligence, Machine Learning, Mathematics, Engineering, or a related disciplineย is preferred.
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