ML Engineer
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Role in brief
Trafmasters seeks a remote ML Engineer with 3+ years of experience in production model deployment. This role focuses on applying machine learning and statistical methods, using Python and various ML tools, within a secure remote environment.
About the role
This ML Engineer position at Trafmasters involves developing and deploying machine learning models into production. The role requires a solid background in data science and a strong grasp of mathematical concepts, including probability theory and statistical analysis. You will be expected to work with fat-tailed data and utilize Monte Carlo simulations as part of your model development process.
The work involves using Python for coding and leveraging specific ML libraries such as CatBoost and LightGBM. Data handling will include querying ClickHouse for complex data extraction. The role also requires familiarity with MLOps tools like Docker, Airflow or Prefect for workflow orchestration, MLflow for model lifecycle management, and FastAPI for API development.
Success in this role means effectively deploying and maintaining ML models that contribute to Trafmasters' objectives. This includes ensuring models are robust, performant, and integrated smoothly into existing systems. The work is conducted entirely remotely, with a requirement to operate within a secure remote desktop environment.
Skills that matter here
- Python: This role requires excellent Python skills for developing and implementing machine learning models and related infrastructure.
- CatBoost: Experience with CatBoost is needed for building and deploying gradient boosting models in production.
- LightGBM: Proficiency in LightGBM is essential for developing high-performance gradient boosting models.
- ClickHouse: The role involves writing complex queries to extract and manipulate data from ClickHouse for model training and analysis.
- Docker: Familiarity with Docker is required for containerizing ML applications and ensuring consistent deployment environments.
- Airflow: Experience with Airflow (or Prefect) is necessary for orchestrating and managing ML pipelines and workflows.
Who this role suits
- A candidate with a strong academic background in mathematics, particularly in probability and statistics.
- Someone who enjoys the challenge of deploying and maintaining machine learning models in a production setting.
- An individual comfortable working independently and securely within a remote desktop environment.
- A person who values precision in data handling and model development, especially with complex data distributions.
From the employer
Requirements
- 3+ years of experience in ML/Data Science with production model deployment.
- Excellent Python skills.
- Experience with CatBoost/LightGBM.
- Proficient in complex queries to ClickHouse.
- Familiarity with Docker, Airflow/Prefect, MLflow, FastAPI.
- Strong mathematical background: probability theory, mathematical statistics, Monte Carlo simulations, working with fat-tailed data.
- Willingness to work in a secure environment via remote desktop (WDS/RDS).
Questions about this role
What is the remote work policy for this position?
This is a fully remote position, but it requires working within a secure environment via remote desktop (WDS/RDS).
What level of experience is required for this role?
Candidates should have a middle seniority level with 3+ years of experience in ML/Data Science, including production model deployment.
What specific skills are important for this ML Engineer role?
Key skills include excellent Python proficiency, experience with CatBoost/LightGBM, complex querying in ClickHouse, and familiarity with Docker, Airflow/Prefect, MLflow, and FastAPI. A strong mathematical background is also required.
✓ Apply here in a minute. Your application and answers go straight to the hiring person, so you never land in a spam pile.