Machine Learning Engineer
At a glance
Mid-level Machine Learning Engineering role at Pomelo. Remote · full-time.
Pay not stated Machine Learning Engineer salary
remoteroles summary, based on the employer's posting.
What you'll do
- Build and tune machine learning models for production use
- Prepare datasets, create features and explore data to support model development
- Create ML pipelines for training, validation and deployment, then track results and retrain when needed
- Work with data, software and product teams to integrate ML solutions
- Set up engineering environments and shape scalable APIs and database architecture
What you bring
- A bachelor’s degree in a technical discipline
- Strong Python skills and experience with frameworks such as TensorFlow, PyTorch or scikit-learn
- A solid grasp of ML methods, statistics and probability
- Hands-on experience preparing data, engineering features and evaluating models
- You can work US hours Monday through Friday, eight hours each day
Who this fits
This hands-on role suits a mid-level engineer who can work independently and collaborate with a team. The schedule requires US time zone availability on weekdays for eight hours a day.
From the employer
ABOUT THE ROLE
We are seeking an experienced Machine Learning Engineer to design, develop, and deploy machine learning models for real-world applications. The ideal candidate is passionate about AI, has strong software engineering skills, and can translate business or research requirements into production-ready solutions. This is a hands-on engineering role, where you’ll work on environment setup, scalable API design, and database architecture.
KEY RESPONSIBILITIES
- Design, implement, and optimize machine learning models for production environments
- Collaborate with data engineers, software engineers, and product teams to integrate ML solutions
- Perform data preprocessing, feature engineering, and exploratory data analysis
- Develop and maintain ML pipelines, including model training, validation, and deployment
- Monitor model performance and implement improvements or retraining as needed
- Stay up-to-date with the latest ML research, techniques, and best practices
- Contribute to technical documentation and knowledge sharing
QUALIFICATIONS
- Bachelor’s degree in a technical field
- Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Solid understanding of machine learning algorithms, statistics, and probability
- Experience with data preprocessing, feature engineering, and model evaluation
- Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) is a plus
- Ability to write clean, maintainable, and well-documented code
- Excellent problem-solving skills and ability to work independently or in a team
- Ability to work in a US time zone, Monday to Friday (8 hours per day)
NICE-TO-HAVES
- Master’s or PhD in Computer Science, Machine Learning, or a related field
- Experience in NLP, computer vision, recommendation systems, or reinforcement learning
- Exposure to MLOps tools and workflows
BENEFITS
- Competitive pay, always in US dollars
- Work remotely from the comfort of your home
- Paid holidays and time off
- Performance bonus
- Global exposure to the world’s best companies
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