Senior machine learning engineer

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.

• At least3–5 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, Driven Data, or equivalent)

• Evidence of top-tier performance in ML competitions (Kaggle medals, finalist placements, leader board rankings)

• Strong proficiency in
  Python
  ,
  PyTorch/Tensor Flow
  , 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

• Kaggle
  Grand master
  ,
  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: