Senior Machine Learning Engineer - LLM Evaluation / Task Creations [$21/hr]
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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 35+ years of applied ML experience or a strong record in competitive ML, and must be based in India.
Responsibilities
- Frame unique ML problems for enhancing ML capabilities of LLMs
- Design, build, and optimise machine learning models for classification, prediction, NLP, recommendation, or generative tasks
- Run rapid experimentation cycles, evaluate model performance, and iterate continuously
- Conduct advanced feature engineering and data preprocessing
- Implement adversarial testing, model robustness checks, and bias evaluations
- Fine-tune, evaluate, and deploy transformer-based models where necessary
- Maintain clear documentation of datasets, experiments, and model decisions
- Stay updated on the latest ML research, tools, and techniques to push modelling capabilities forward
Required Qualifications
- At least
35 years
of full-time experience in machine learning model development - Technical degree in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related field
- Demonstrated competitive machine learning experience (Kaggle, DrivenData, or equivalent)
- Evidence of top-tier performance in ML competitions (Kaggle medals, finalist placements, leaderboard rankings)
- Strong proficiency in
Python
, PyTorch/TensorFlow
, and modern ML/NLP frameworks - Solid understanding of ML fundamentals: statistics, optimisation, model evaluation, architectures
- Experience with distributed training, ML pipelines, and experiment tracking
- Strong problem-solving skills and algorithmic thinking
- Experience working with cloud environments (AWS/GCP/Azure)
- Exceptional analytical, communication, and interpersonal skills
- Ability to clearly explain modelling decisions, tradeoffs, and evaluation results
- Fluency in English
Preferred / Nice to Have
- Kaggle
Grandmaster
, Master
, or multiple Gold Medals
- Experience creating benchmarks, evaluations, or ML challenge problems
- Background in generative models, LLMs, or multimodal learning
- Experience with large-scale distributed training
- Prior experience in AI research, ML platforms, or infrastructure teams
- Contributions to technical blogs, open-source projects, or research publications
- Prior mentorship or technical leadership experience
- Published research papers (conference or journal)
- Experience with LLM fine-tuning, vector databases, or generative AI workflows
- Familiarity with MLOps tools: Weights & Biases, MLflow, Airflow, Docker, etc.
- Experience optimising inference performance and deploying models at scale
Why Join
- Gain exposure to cutting-edge AI research workflows, collaborating closely with data scientists, ML engineers, and research leaders shaping next-generation AI systems
- Work on high-impact machine learning challenges while experimenting with advanced modelling strategies, new analytical methods, and competition-grade validation techniques
- Collaborate with world-class AI labs and technical teams operating at the frontier of forecasting, experimentation, tabular ML, and multimodal analytics
- 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 systems - Fully remote and globally flexible optimised for deep technical work, async collaboration, and high-output research environments
