Machine Learning Engineer | $750–€“$1,200 USD/month | Fully Remote

Location: Fully remote — you can be based anywhere in the world
Hours: Flexible –€” async-friendly, no fixed overlap required
Salary: $750–€“$1,200 USD/month
Contract: Full-time, permanent

The location shown on this listing is where the client is based. This role is fully remote — no relocation required. Candidates from anywhere are welcome to apply.

About the company

Remote World is partnering with a climate-tech startup optimising energy grid dispatch across the UK –€” a UK-based employer –€” to hire for this role. Their ML models help grid operators reduce carbon output by predicting renewable generation and demand in real time –€” and they're expanding to Europe.

The role

You'll build, evaluate and deploy machine learning models that improve energy forecasting accuracy. Working alongside senior ML engineers and data scientists, you'll own experiments end to end –€” from data preparation through to model deployment in production.

What you'll do

• Build and evaluate ML models for time-series forecasting
• Prepare and validate training datasets from weather and grid data sources
• Deploy models to production using cloud ML infrastructure
• Monitor model performance and retrain on schedule
• Document experiments and results clearly for the research team

What you'll need

• Strong Python and experience with scikit-learn, XGBoost or PyTorch
• Comfortable with data wrangling using Pandas and NumPy
• Understanding of model evaluation and cross-validation
• Familiarity with version control for models and experiments (MLflow or similar)
• Good async written communication

Nice to have

• Experience with time-series forecasting
• Familiarity with cloud ML platforms (SageMaker or Vertex AI)

What you'll get

• $750–€“$1,200 USD/month, paid monthly
• Fully remote –€” work from anywhere
• Fully async –€” no fixed meeting schedule
• $500/year learning budget
• Home office equipment stipend

Apply through Remote World –€” send your CV and a short note on why you're a great fit. If you don't tick every box, apply anyway. We hire for potential and attitude as much as experience.