Original listing text, shown exactly as published by the company.
Key Responsibilities
- Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering
- Develop, train, evaluate, and iterate on machine learning models across classification, regression, clustering, and NLP tasks
- Fine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasets
- Build and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelines
- Work with structured and unstructured data at scale — including text, tabular, and time-series data
- Monitor model performance in production and implement retraining and drift-detection strategies
- Collaborate with engineering and product teams to translate data insights into actionable AI features
- Document data schemas, model architectures, and pipeline logic clearly and thoroughly
Required Qualifications
- Strong Python skills with hands-on experience in core ML libraries (scikit-learn, PyTorch, TensorFlow, or similar)
- Solid data engineering experience — SQL, ETL pipelines, and working with large-scale datasets
- Practical experience with model training, evaluation, hyperparameter tuning, and deployment
- Familiarity with LLMs and transformer-based architectures; experience with fine-tuning or prompt engineering in production contexts
- Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC, or similar)
- Strong grasp of statistical concepts, data quality principles, and model performance metrics
- Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered.
Preferred Qualifications
- Experience with distributed data processing frameworks (Spark, Dask, or similar)
- Familiarity with vector databases and embedding-based retrieval systems
- Background working with real-time or streaming data pipelines (Kafka, Flink, or similar)
- Exposure to cloud-native ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
- Experience with data governance, lineage tracking, or compliance-aware data workflows
Tools & Technology
- Python, SQL, and core ML/data libraries (PyTorch, scikit-learn, Pandas, NumPy)
- MLOps: MLflow, Weights & Biases, DVC, or equivalent
- Data warehouses and lakes: Snowflake, BigQuery, Redshift, or similar
- LLM platforms: Hugging Face, OpenAI, Anthropic, or similar
- Cloud infrastructure: AWS, GCP, or Azure
- Google Workspace, Slack, Zoom, and remote collaboration tools
Please note: It is crucial that you complete the application form in full. As part of the application process, you will be required to record a video. If your application is successful, you will receive an email confirming next steps — the video is the first step of the interview process. If you do not record a video, we will not be able to consider you for ANY open roles.
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