Senior Machine Learning Engineer

Job Description Hiring on behalf of a leading AI research lab to bring on highly skilled Machine Learning Engineers with a proven record of building, training, and evaluating high-performance ML systems in real-world environments. In this role, you will design, implement, and curate high-quality machine learning datasets, tasks, and evaluation workflows that power the training and benchmarking of advanced AI systems. This position is ideal for engineers who have excelled in competitive machine learning settings such as Kaggle, possess deep modelling intuition, and can translate complex real-world problem statements into robust, well-structured ML pipelines and datasets. You will work closely with researchers and engineers to develop realistic ML problems, ensure dataset quality, and drive reproducible, high-impact experimentation. Candidates should have 3+ years of applied ML experience or a strong record in competitive ML, and must be based in India. Ideal applicants are proficient in Python, experienced in building reproducible pipelines, and familiar with benchmarking frameworks, scoring methodologies, and ML evaluation best practices . Responsibiliti - esFrame unique ML problems for enhancing ML capabilities of LLM - s.Design, build, and optimise machine learning models for classification, prediction, NLP, recommendation, or generative task - s.Run rapid experimentation cycles, evaluate model performance, and iterate continuousl - y.Conduct advanced feature engineering and data preprocessin - g.Implement adversarial testing, model robustness checks, and bias evaluation - s.Fine-tune, evaluate, and deploy transformer-based models where necessar - y.Maintain clear documentation of datasets, experiments, and model decision - s.Stay updated on the latest ML research, tools, and techniques to push modelling capabilities forwar d.Required Qualificatio - nsAt least 3 years of full-time experience in machine learning model developme - ntTechnical degree in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related fie - ldDemonstrated competitive machine learning experience (Kaggle, DrivenData, or equivalen - t)Evidence of top-tier performance in ML competitions (Kaggle medals, finalist placements, leaderboard ranking - s)Strong proficiency in Python, PyTorch/TensorFlow, and modern ML/NLP framewor - ksSolid understanding of ML fundamentals: statistics, optimisation, model evaluation, architectur - esExperience with distributed training, ML pipelines, and experiment tracki - ngStrong problem-solving skills and algorithmic thinki - ngExperience working with cloud environments (AWS/GCP/Azur - e)Exceptional analytical, communication, and interpersonal skil - lsAbility to clearly explain modelling decisions, tradeoffs, and evaluation resul - tsFluency in Engli sh Preferred / Nice to H - aveKaggle Grandmaster, Master, or multiple Gold Med - alsExperience creating benchmarks, evaluations, or ML challenge probl - emsBackground in generative models, LLMs, or multimodal learn - ingExperience with large-scale distributed train - ingPrior experience in AI research, ML platforms, or infrastructure te - amsContributions to technical blogs, open-source projects, or research publicati - onsPrior mentorship or technical leadership experie - ncePublished research papers (conference or journ - al)Experience with LLM fine-tuning, vector databases, or generative AI workfl - owsFamiliarity with MLOps tools: Weights & Biases, MLflow, Airflow, Docker, e - tc.Experience optimising inference performance and deploying models at sc aleWhy J - oinGain exposure to cutting-edge AI research workflows, collaborating closely with data scientists, ML engineers, and research leaders shaping next-generation AI syste - ms.Work on high-impact machine learning challenges while experimenting with advanced modelling strategies, new analytical methods, and competition-grade validation techniqu - es.Collaborate with world-class AI labs and technical teams operating at the frontier of forecasting, experimentation, tabular ML, and multimodal analyti - cs.Flexible engagement options (3040 hrs/week or full-time) ideal for ML engineers eager to apply Kaggle-level problem solving to real-world, production-grade AI syste - ms.Fully remote and globally flexible optimised for deep technical work, async collaboration, and high-output research environmen ts.