Mercor
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 3–5 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
Apply here: https://work.mercor.com/jobs/list_AAABmwGdnqiMMld4ODBIgpFh?referralCode=ac03e252-9fa9-4c88-894b-92fdd2994e34&utm_source=share&utm_medium=referral&utm_campaign=job_referral
#J-18808-Ljbffr
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 3–5 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
Apply here: https://work.mercor.com/jobs/list_AAABmwGdnqiMMld4ODBIgpFh?referralCode=ac03e252-9fa9-4c88-894b-92fdd2994e34&utm_source=share&utm_medium=referral&utm_campaign=job_referral
#J-18808-Ljbffr