Position: ML Engineer
Company: WorkNest
Experience required: 5y
Category: Python
English level: upper
Published: 2022-09-01T00:00:00+03:00

**Requirements :**
 
- 5+ years industrial experience with ML, including training, testing, deployment, and monitoring real-time (or near real-time) machine learning models in production.
- Proven experience with modern DL techniques and Gradient Boosting Trees.
- Excellent Python coding skills.
- Proficiency with ML libraries (Tensorflow, Pytorch, LightGBM, Scikit-learn)
- Vast practical experience with tabular data and time series.
- Experience in Microservices architecture, cloud technologies, Docker, Kubernetes.
- Fluent English

**As an advantage :**

- M.Sc. in computer science with a thesis in ML. PhD – advantage.
- Experience with ML “understanding” techniques: explainability, confidence, drift, etc – big advantage.
- Experience designing and implementing AutoML flows/frameworks – big advantage.
- Experience with data pipelines / big data – advantage.

**Expectations :**

- Own, design, and develop Wand AI ML pipelines: be responsible for efficient models training, testing, deployment, and monitoring.
- Apply your scientific knowledge to analyze and collect data, identify problems, devise solutions and construct methodologies.
- Develop machine learning and deep learning models for tabular data (later: time series, NLP, CV, etc.).
- Learn and Implement cutting-edge ML algorithms.
- Create and automate data pipelines including data extraction, validation & anomaly detection, indexing, and other data-related tasks.
- Follow your project from ideation to implementation.
- Contribute to a wide variety of projects using a range of technologies and tools.

**We are ready to offer :**

- Competitive salary based on your experience level
- Professional and career growth opportunities
- Opportunity to work and communicate directly with the customers
- Your health support via gym compensation and vacation policy
- Free English classes twice a week