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Machine Learning Engineer

Rol remoto de Machine Learning Engineer con fit claro de ubicación del candidato.

Publicado26 jul 2026

Países elegibles9 países aceptados

Señal de seniorityMiddle

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

ArgentinaBeliceColombiaRepública DominicanaHondurasMéxico+3 más

Resumen del rol

Contenido del rol extraído en secciones para revisar más rápido.

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

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.

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