AI/ML Researcher

NK Securities Research is a leading financial firm that leverages cutting-edge technology, data-driven research, and sophisticated algorithms to trade financial markets. Founded in 2011, we have built deep expertise in high-frequency trading across different asset classes.

We are a fast-growing organization with a strong research and technology culture. The firm’s lean and meritocratic structure enables individuals to work closely with experienced researchers, traders, and engineers, while taking ownership of high-impact problems from an early stage.

Role Overview

We are looking for an AI/ML Researcher to join our central AI/ML research team in India. This is a research-intensive role suited for individuals who enjoy mathematical problem-solving, experimentation, and working with complex, noisy, high-dimensional datasets. The role will involve close collaboration with quantitative researchers, traders, engineers, and business stakeholders to identify areas where AI/ML can create meaningful value across the firm.

The ideal candidate should be comfortable working on open-ended research problems, converting ideas into structured experiments, and building models that can be evaluated rigorously in real-world settings.

Key Responsibilities

As an AI/ML Researcher, you will:

• Work on our GPU cluster to research and understand the fine-grained behavior of large neural networks and LLM training.
• Fine-tune existing open-source models and build benchmarks to evaluate their performance.
• Explore and analyze large-scale financial market datasets.
• Build ML and statistical models for pattern discovery, prediction, and signal generation.
• Work on short-horizon, noisy, and non-stationary market data.
• Conduct feature engineering, model validation, and performance evaluation.
• Study pricing, market microstructure, and execution-related research problems.
• Convert research ideas into structured experiments with measurable outcomes.
• Collaborate with quantitative researchers, traders, and engineers to translate research into practical use cases.
• Present research findings clearly to technical and business stakeholders.
• Contribute to building reusable research frameworks, tools, and best practices for the AI/ML research function.