We are hiring highly skilled Machine Learning Engineers to work with a leading AI research organization on the design, development, and evaluation of advanced machine learning systems. This role focuses on building high-quality datasets, realistic ML tasks, and rigorous evaluation workflows that directly power the training, benchmarking, and improvement of next-generation AI models, including large language models.

This position is ideal for engineers with strong applied machine learning experience and deep modeling intuitionparticularly those who have excelled in competitive ML environments such as Kaggle or similar platforms. You will work at the intersection of research and engineering, transforming complex, real-world problem statements into well-structured datasets, reproducible pipelines, and measurable evaluation frameworks.

The work you do in this role will have a direct impact on how modern AI systems are trained, tested, and compared. You will collaborate closely with researchers and engineers to ensure that ML problems are realistic, datasets are clean and representative, and experiments are reproducible, interpretable, and scalable.

Location requirement : Candidates must be based in India.

Role Overview :

As a Machine Learning Engineer, you will be responsible for framing meaningful ML problems, building and evaluating models, and creating robust experimentation pipelines. Your focus will go beyond model accuracyyou will help ensure dataset quality, fairness, robustness, and clarity of evaluation metrics.

This role demands strong hands-on skills in Python, modern ML frameworks, and experimental design, along with the ability to think critically about tradeoffs, biases, and failure modes in machine learning systems.

Key Responsibilities :

- Design and frame unique, high-impact machine learning problems that help enhance the capabilities and reasoning performance of advanced AI systems, including LLMs.

- Build, train, and optimize machine learning models across a range of tasks such as classification, prediction, natural language processing, recommendation systems, and generative modeling.

- Develop and curate high-quality datasets, ensuring proper labeling, balance, representativeness, and documentation.

- Run rapid experimentation cycles, evaluate model performance using appropriate metrics, and iterate continuously to improve results.

- Perform advanced feature engineering and data preprocessing to extract meaningful signals from complex or noisy data.

- Implement adversarial testing strategies, robustness checks, and bias evaluations to understand model limitations and failure cases.

- Fine-tune, evaluate, and deploy transformer-based models where required, ensuring reproducibility and scalability.

- Maintain clear and structured documentation covering datasets, experimental setups, modeling decisions, and evaluation outcomes.

- Stay current with the latest machine learning research, tools, and techniques, and apply relevant advances to improve modeling approaches and evaluation quality.

Required Qualifications :

- Minimum 2 years of full-time, hands-on experience building and evaluating machine learning models in production or research-oriented environments.

- A technical degree in Computer Science, Electrical Engineering, Statistics, Mathematics, or a closely related field.

- Demonstrated experience in competitive machine learning (Kaggle, DrivenData, or equivalent platforms).

- Evidence of strong performance in ML competitions, such as leaderboard rankings, finalist placements, or medals.

- Strong proficiency in Python, along with hands-on experience using PyTorch, TensorFlow, or similar modern ML frameworks.

- Solid understanding of core machine learning fundamentals, including statistics, optimization, model architectures, and evaluation methodologies.

- Experience building reproducible ML pipelines, running experiments at scale, and tracking results systematically.

- Familiarity with distributed training concepts and working in cloud-based environments such as AWS, GCP, or Azure.

- Strong analytical thinking, problem-solving ability, and algorithmic reasoning.

- Excellent communication skills, with the ability to clearly explain modeling choices, tradeoffs, and evaluation results.

- Fluency in English, both written and spoken.

Preferred / Nice-to-Have Qualifications :

- Kaggle Grandmaster or Master status, or multiple Gold Medals in competitive ML.

- Experience designing ML benchmarks, evaluation suites, or challenge-style problems.

- Background in generative models, large language models (LLMs), or multimodal learning.

- Hands-on experience with large-scale distributed training and performance optimization.

- Prior work within AI research teams, ML platforms, or infrastructure-focused engineering groups.

- Contributions to open-source projects, technical blogs, or published research papers (conference or journal).

- Experience mentoring junior engineers or providing technical leadership.

- Familiarity with LLM fine-tuning, vector databases, and generative AI workflows.

- Knowledge of MLOps tools such as Weights & Biases, MLflow, Airflow, Docker, or similar systems.

- Experience optimizing inference performance and deploying ML models at scale.