Senior Machine Learning Engineer LLM Evaluation Task Creations

Bengaluru - India

Monthly Salary: Not Disclosed

Posted on: 10 hours ago

Vacancies: 1 Vacancy

Job Summary

Role Description

Mercor is 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 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.

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 3 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.

Role Description Mercor is 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 this role you will design implement and curate high-quality machine learn...

Role Description

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 3 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.