As an ML Engineer at Greenscreens.ai, you’ll play an important position in advancing logistics expertise by growing and optimizing ML fashions that handle new enterprise challenges. You can be chargeable for guaranteeing the effectivity and accuracy of our deployed fashions, scaling their efficiency, and automating ML pipelines. Your work will contain constructing and managing the infrastructure for coaching fashions, conducting analysis, and making use of findings immediately to enhance shopper options. Moreover, you’ll improve our predictive fashions, discover new options to refine predictions, and combine complicated enterprise logic into our processes. Your contributions will form the way forward for our ML-based options and drive innovation within the logistics business.
Obligations
- Analysis and determine new enterprise options to reinforce prediction accuracy
- Improve Fee Engine by means of algorithm manipulation, characteristic experimentation, and analysis to optimize information filtering and predictive mannequin high quality.
- Monitor and keep deployed ML fashions, guaranteeing accuracy and effectivity
- Automate ML pipelines and handle your entire mannequin lifecycle.
- Develop complicated enterprise logic in Python to combine fashions into an organization’s processes.
- Scale and optimize the efficiency of current fashions (RPS, reminiscence consumption)
- The first focus of your work shall be on tabular information
Necessities
- 3+ years of expertise as a Knowledge Scientist, ML Engineer, or in an identical position.
- Python, SQL,Git
- Neural networks, time collection, gradient boosting, and random forest.
- Linear algebra, chance, statistics, optimization
- Higher-intermediate English and Russian proficiency for efficient communication within the groups.
- Superior proficiency in each Russian and English is required—no exceptions.
Fascinating Technical abilities
- Unit testing
- AWS S3, Docker, Kubernetes
- Expertise in logistics
- Lively engagement with business articles and analysis papers
- Participation in competitions (e.g., Kaggle)
- Hyperparameter tuning strategies
- Anomaly detection
{Qualifications}
- Bachelor’s or Grasp’s diploma in Laptop Science, Engineering, Arithmetic, or a associated discipline.
Advantages
Distant Work: Capacity to work from wherever on the planet or in our workplace in Vilnius. Nonetheless, please word that there are restrictions on working from Russia and Belarus.
Choices Program: Take part in our choices program, permitting you to share within the development and success of our startup.
Annual personal medical health insurance allowance
PTO: As much as 4 weeks of absolutely paid go away per calendar 12 months