Junior Machine Learning Engineer
Fully Remote | Full-Time
We’re looking for a Junior Machine Learning Engineer to join Spotter and help build and improve AI-powered products.
You’ll work with real-world datasets, develop and evaluate machine learning models, experiment with ways to improve their performance, and work alongside software engineers to bring ML solutions into production.
This role is a great fit for someone early in their ML career who has strong fundamentals, enjoys solving practical problems, and wants to gain hands-on experience building AI products used in the real world.
What You’ll Do
- Develop, train, test, and evaluate machine learning models.
- Prepare, clean, and analyze datasets for model training.
- Run experiments and improve model performance.
- Help deploy and maintain ML models in production.
- Collaborate with software engineers to integrate ML solutions into products.
- Monitor model performance and identify opportunities for improvement.
- Explore new machine learning techniques and technologies that could improve our products.
What We’re Looking For
- Strong Python fundamentals.
- Understanding of core machine learning concepts and algorithms.
- Experience with ML libraries such as PyTorch, TensorFlow, or scikit-learn.
- Comfortable working with data and writing clean, maintainable code.
- Familiarity with Git.
- Strong analytical and problem-solving skills.
- Good written and verbal English communication.
- 1+ year of experience in machine learning, software engineering, data science, or a related technical role — or strong personal/academic projects demonstrating practical ML skills.
Nice to Have
- Experience working with large language models (LLMs).
- Familiarity with AWS, GCP, or Azure.
- Experience with SQL.
- Experience deploying ML models.
- AI/ML personal projects, research, hackathons, or open-source contributions.
What We Offer
- Fully remote work.
- Flexible working environment.
- Opportunity to work on real AI-powered products.
- Hands-on experience solving challenging technical problems.
- Significant room to learn, experiment, and grow.
- Collaborative and fast-moving team.
- Competitive compensation based on experience.
You don’t need to check every box. If you have strong ML fundamentals and can demonstrate what you’ve built, we’d still like to hear from you
