Position: ML Engineer, Personalization
Company: Neurons Lab
Experience required: 3y
English level: upper

**Objective**
Our customer is building a customer engagement platform for responsible gambling operators. 
The platform should support games for years after they’re in the hands of gamers worldwide. The team works closely with the game development, business, and internal service teams to produce insights and models that enable us to make the most epic gamer experiences. 

We start with deep data analysis to understand our customers, their objectives, and any issues they might have. We then use various data points and advanced machine learning algorithms, to come up with the best possible option for each client. We experiment, iterate, and build fully automated solutions, based on algorithms that self-improve with time.

**Key results**
- Grow CSI to 100%
- Grow % of projects delivered in project terms (margin/deadline/scope) to 100%

**Challenges & responsibilities**
- Research and develop state-of-the-art machine learning solutions for personalization/recommendation systems to enhance business and gaming objectives.
- Build and evaluate machine learning models to improve in-game features and boost product metrics
- Work closely with the customer, data scientists, product owner,  software engineers, and data engineers to deploy, automate, and maintain models seamlessly on production systems.
- Communicate about machine learning models and products to both technical and non-technical audiences.
- Use ML to build a rich, semantic understanding of the various kinds of gaming content

**Skills**
- research and paper analysis
- explanation and presentation
- machine learning and engineering
- advanced English
- documentation development

**Knowledge**
- A track record of original contributions to research communities of ML and AI.
- Experience in the industry of gambling/gaming/e-commerce  engagement products

**Experience**
- Master’s or Ph.D. with a specialization in ML and AI.
- Hands-on experience with one or more of the following: collaborative filtering, user-based, and item-based personalization models, a deep neural network for a recommendation system, transformers for textual analytics, etc.
- Hands-on applied or research experience in developing machine learning models on a large dataset of user interactions (view, play activities, purchase, click-through, usage, etc.).
- Design, analyze, and run both simulated and live Machine Learning experiments (multi-armed / contextual bandits) to drive KPI improvement
- Proficient in Python and experienced in at least one other higher-level programming language like Java, C++, C#, Scala, etc.
- Practical experience with Tensorflow, Pytorch, or Keras.

**Terms & conditions**
Rate: $35-75 per hour
Allocation: 40-80 h/month