Job Description

• Bangalore, Karnataka

Exciting Opportunity at Eloelo: Join the Future of Social Media with India’s largest Live Streaming & Social Gaming Platform

Are you ready to be a part of the dynamic world of live streaming and social gaming? Look no further! Eloelo, an innovative Indian platform founded in February 2020 by ex-Flipkart executives Akshay Dubey and Saurabh Pandey, is on the lookout for passionate individuals to join our growing team in Bangalore.

About Us:

Eloelo stands at the forefront of multi-host video and audio rooms, offering a unique blend of interactive experiences, including chat rooms, PK challenges, audio rooms, and captivating live games like Lucky 7, Tambola, Tol Mol Ke Bol, and Chidiya Udd. Our platform has successfully attracted audiences from all corners of India, providing a space for social connections and immersive gaming.

Recent Milestone:

In pursuit of excellence, Eloelo has secured a significant milestone by raising $22Mn in the month of October 2023  from a diverse group of investors, including Lumikai, Waterbridge Capital, Courtside Ventures, Griffin Gaming Partners, and other esteemed new and existing contributors.

Why Eloelo?

• Be a part of a team that thrives on creativity and innovation in the live streaming and social gaming space.
• Build a new-age social network, almost like a digital third place that is safe, trusted & encourages interactivity
• Create at the intersection of RTC, AI, Games, Audio/ Video, ML & Chat which empowers creators to grow and monetize their presence
• Working with a world class team, high performance team that constantly pushes boundaries and limits , redefines what is possible 
• Fun and work at the same place with amazing work culture , flexible timings and vibrant atmosphere

We are seeking an experienced Machine Learning Engineer to join our Live Entertainment team. As a key member, you will play a pivotal role in developing and enhancing our machine-learning models to create personalized recommendations and drive engaging push-notification strategies for our users. The ideal candidate should have a strong background in recommendation systems, working knowledge of various ML algorithms, and a passion for leveraging data-driven insights in the Live Entertainment industry.

Responsibilities:

1. User Profiling:

• Collect and analyze user data, including historical event attendance, preferred genres, and user interactions.
• Utilize this data to create comprehensive user profiles that capture individual preferences and behavior.

1. Content Representation:

• Develop a robust content representation model, mapping live events and performances to a feature-rich space.
• Incorporate metadata, user reviews, and social media sentiments to enhance the understanding of each event.

1. Collaborative Filtering:

• Implement collaborative filtering algorithms to identify patterns and similarities between users with similar preferences.
• Leverage user-item interaction matrices to make personalized recommendations based on the preferences of similar users.

1. Content-Based Filtering:

• Use content-based filtering techniques to recommend events similar to those a user has previously enjoyed.
• Combine content-based and collaborative filtering to provide a well-rounded set of recommendations.

1. Real-time Behavior Analysis:

•  Monitor real-time user behavior like searches, clicks, and event views.
•  Update recommendations in real time to reflect the user's current interests and preferences.

1. Push Notification Strategy:

• Implement a push notification strategy to proactively engage users with personalized content.
• Notify users about upcoming events that align with their preferences, new releases from their favorite artists, or special promotions.

1. A/B Testing:

• Conduct A/B testing to evaluate the effectiveness of different recommendation algorithms and push-notification strategies.
• Optimize the system based on user engagement metrics and feedback.

1. ML Algorithm Implementation:

• Apply a deep understanding of machine learning algorithms to develop robust models for content representation and user profiling.
• Stay abreast of the latest advancements in ML and implement innovative techniques to improve recommendation accuracy.

1. Data Analysis and Feature Engineering:

• Conduct exploratory data analysis to identify relevant features for model development.
• Engineer features that capture user preferences, event characteristics, and real-time interactions.

1. Model Evaluation and Optimization:

• Evaluate the performance of recommendation models using relevant metrics.
• Optimize models through continuous iteration, A/B testing, and incorporating user feedback.

1. Cross-functional Collaboration:

• Collaborate with software engineers, data scientists, and product managers to integrate machine learning models into our live entertainment platform.
• Communicate complex technical concepts to non-technical stakeholders effectively.

Requirements:

• Bachlors in Computer Science, Statistics, or a related field.
• 3-6 years of industry experience in machine learning with a focus on recommendation systems.
• Proficiency in programming languages such as Python or R.
• Strong understanding of machine learning libraries and frameworks (e.g., TensorFlow, PyTorch).
• Experience with big data technologies and distributed computing.
• Proven track record of implementing successful machine learning projects in a production environment.

Preferred Qualifications:

• Familiarity with natural language processing (NLP) for content understanding.
• Knowledge of cloud platforms (e.g., AWS, Azure) and containerization (e.g., Docker, Kubernetes).
• Previous experience with live streaming or entertainment platforms.