Online Machine Learning Engineer Jobs (Work From Home)
Verified machine learning engineer jobs with international employers. Remote, paid in USD, from $12/hr. Apply direct on FindTalent, free for talent.
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How much do online machine learning engineer jobs pay?
| Experience level | Hourly (USD) | Monthly (PHP, full-time) | |
|---|---|---|---|
| Entry level (0–1 yr) | $12–19/hr | ₱114,000–181,000 | |
| Experienced (1–3 yrs) | $19–28/hr | ₱181,000–266,000 | |
| Specialist (3+ yrs / niche) | $28–44/hr | ₱266,000–418,000 |
Typical ranges for remote Filipino specialists, reviewed July 2026. Paid in USD, direct to you.
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Skills employers look for in machine learning engineers
- Python and ML library proficiency: Scikit-learn, PyTorch, or TensorFlow. Verify production-level usage, not just familiarity; ask for a trained model they have deployed to a real inference endpoint and how it performed.
- Feature engineering and data preparation: The ability to identify and engineer predictive features from raw data is a core ML skill. Ask how they approached feature selection for a past classification problem and what impact it had on model performance.
- Model evaluation and metric selection: Choosing the right evaluation metric for the business problem; accuracy is misleading for imbalanced classes; precision/recall tradeoffs matter for cost-asymmetric errors; ask how they select metrics for a new problem.
- Experiment tracking and model versioning: MLflow, Weights & Biases, or Neptune; the ability to track training runs, compare experiments, and reproduce results is essential for iterative ML development.
- Model deployment to production: Deploying a model as a REST API via FastAPI, AWS SageMaker, or Hugging Face Inference Endpoints. Ask for a production deployment they built and any latency or availability issues they resolved.
- NLP and text model fine-tuning (if applicable): Fine-tuning BERT, RoBERTa, or instruction-tuned LLMs on domain-specific classification or generation tasks. Ask for a fine-tuning project and the evaluation methodology they used to measure improvement.
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