Machine Learning Engineer
- Full-time
- Mid level
- Remote (worldwide)
Remote Machine Learning Engineer at MacroCoderz: fine-tune, evaluate and serve models - from classifiers to open-source LLMs - in production systems.
Role at a glance
| Employment type | Full-time |
|---|---|
| Level | Mid level |
| Experience | 2+ years |
| Location | Remote (worldwide) |
| Team | Data and machine learning |
| Education | Degree in computer science, data science or a related field |
| Posted | 10 October 2026 |
About the role
Not every problem should go to a large hosted model. As a Machine Learning Engineer you build the models that make our systems faster, cheaper or more private: classifiers and rerankers, fine-tuned open-source LLMs, and embedding models adapted to a client's domain.
You take a model from dataset to production endpoint, and you prove it beats the simpler option before it ships.
What you will do
- Fine-tune and evaluate models, including open-source LLMs and embedding models.
- Build training and evaluation datasets with the AI Engineering team.
- Serve models efficiently and monitor them in production.
- Compare custom models against hosted APIs on quality, cost and latency.
- Keep experiments reproducible and documented.
What we are looking for
- 2+ years of professional ML engineering.
- Strong Python and PyTorch.
- Experience fine-tuning transformer models.
- Solid grasp of evaluation methodology and overfitting.
Nice to have
- LoRA/QLoRA and the Hugging Face ecosystem.
- Model serving with vLLM, TGI or similar.
- Published research or open-source contributions.
Skills and tools
- Python
- PyTorch
- Hugging Face
- Fine-tuning
- LoRA
- Model serving
- MLflow
Who this role suits
ML engineers and researchers who want their models in production, and data scientists ready to move into engineering.
The work behind this role
Machine Learning Engineer: common questions
Yes. The Machine Learning Engineer role at MacroCoderz is fully remote and open to applicants worldwide. It is a full-time, mid level position.
2+ years (mid level). Education: Degree in computer science, data science or a related field. Who it suits: ML engineers and researchers who want their models in production, and data scientists ready to move into engineering.
Rarely. Most of the work is fine-tuning and adapting existing models - open-source LLMs, embedding models, classifiers and rerankers - then evaluating and serving them. Training from scratch is reserved for the few problems where nothing existing fits.
Use the application form on this page. It asks for your contact details, city, LinkedIn profile, expected salary, how you heard about the role, a short description of two or three projects you have worked on, and your CV as a PDF or Word file. An engineer reviews every application and emails shortlisted applicants to arrange a call.