Algorithm Engineer

COMPANY

MeeBoss is a startup revolutionizing the online recruiting industry. Our mission is to streamline the hiring process, empowering both job seekers and employers to connect effectively.

By leveraging cutting-edge technology, MeeBoss is transforming the way companies attract, engage, and hire top talent. Our solutions are tailored to the needs of modern businesses through direct chat with job-ready talent and to connect job seekers to the person behind the job, delivering a seamless, personalized experience - anywhere, anytime.

About the Role

The Recommendation Algorithm Specialist will design, implement, and optimize recommendation systems that personalize user experience and drive engagement. The role involves applying data science, machine learning, and behavioral analytics to develop algorithms capable of delivering context-aware and high-precision recommendations across digital platforms.

Key Responsibilities

• Develop, train, and deploy recommendation algorithms using collaborative filtering, content-based filtering, and hybrid models.
• Analyze large-scale user behavior data to extract actionable insights and improve personalization accuracy.
• Implement A/B testing frameworks to measure model effectiveness and drive continuous enhancements.
• Collaborate with product, engineering, and analytics teams to integrate recommendation systems into live environments.
• Monitor performance metrics (precision, recall, CTR, conversion rate) to ensure relevance and scalability.
• Research and experiment with advanced techniques such as reinforcement learning, graph neural networks, and contextual modeling.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Applied Mathematics, or a related field.
• 3–5 years of hands-on experience developing and deploying recommendation or personalization algorithms.
• Strong proficiency in Python and libraries such as TensorFlow, PyTorch, or Scikit-learn.
• Solid understanding of data structures, algorithms, and large-scale data processing (Spark, Hadoop, or similar).
• Experience with SQL/NoSQL databases and data pipeline tools.
• Knowledge of recommender evaluation metrics and large-scale experimentation methods.
• Strong analytical ability and communication skills for cross-functional collaboration.

Preferred Skills

• Familiarity with user segmentation, deep learning architectures, or embeddings for recommendation.
• Experience working with real-time recommendation engines and big data ecosystems.
• Exposure to MLOps frameworks for model deployment and lifecycle management.

What We Offer

• Competitive compensation and performance-based incentives.
• Opportunities to work on cutting-edge personalization systems.
• Collaborative and data-driven work culture.
• Professional growth through training and conferences in machine learning and AI domains.