Job Description

Job Overview

We are seeking a

highly curious, logic-driven AI / Machine Learning Research Intern

to work on

matchmaking algorithms, recommendation systems, and user compatibility modeling

for a Gen-Z focused dating application.

This is a

research- and experimentation-heavy internship

. The role goes beyond coding -- you will explore

how AI-driven decisions influence human behavior, match quality, and long-term user engagement

.

This opportunity is ideal for candidates looking for

real-world applied AI exposure

, not theoretical or toy dataset work.

Key Responsibilities1. Matchmaking Algorithm Research

Design and experiment with

match scoring logic

using user attributes such as interests, activity patterns, response behavior, and engagement timing Compare

rule-based vs machine learning-based

matchmaking approaches Continuously refine match quality based on experimental outcomes
2. Recommendation System Experiments

Work on

content-based and collaborative filtering

models Implement similarity techniques such as

cosine similarity, KNN, and basic clustering

Evaluate recommendation relevance and ranking quality
3. Cold-Start Problem Solving

Design strategies to match

new users with limited data

Experiment with onboarding signals, inferred preferences, and fallback logic Measure cold-start performance independently
4. Model Evaluation & Metrics

Define and track metrics such as: Match success rate Chat initiation rate Response probability Analyze false positives (poor matches) vs true positives (successful conversations) Support

A/B testing

of algorithm changes
5. Research Documentation

Document each experiment clearly:

hypothesis, approach, results, and learnings

Maintain reusable research notes for future team members Present findings weekly in

simple, non-technical language

6. Cross-Functional Collaboration

Collaborate with

Data Research, Behavioral Research, and UX teams

Translate behavioral insights into algorithm-ready signals Iterate based on product and research feedback
Required Skills & QualificationsTechnical Skills (Mandatory)

Strong

Python programming

skills Basic understanding of

Machine Learning concepts

(supervised & unsupervised learning) Hands-on experience with

Scikit-learn

Understanding of

similarity measures

and basic clustering techniques
Analytical Skills

Strong logical reasoning and problem-solving ability Ability to break complex problems into simple components Comfort with experimentation and learning through iteration

Educational Background

Currently pursuing or recently completed a degree in: Computer Science, Artificial Intelligence, Machine Learning , Data Science Or related technical fields

Preferred (Good to Have)Technical

Knowledge of

recommendation systems

Basic statistics (probability, correlation, distributions) Beginner-level familiarity with

TensorFlow or PyTorch

Experience using

Jupyter Notebooks

Practical Exposure

Prior ML projects (academic, personal, or internship-based) Experience working with real or unclean datasets Interest in consumer apps or social platforms

Soft Skills

High curiosity and learning mindset Ability to explain technical concepts simply Openness to feedback and iteration

Interview Assignment (Mandatory)

Task:

Candidates will receive

8-10 sample user profiles

with basic attributes and activity data.

You will be required to:

Design a

simple match score formula

Rank top matches for each user Explain the logic in real-world dating context

Evaluation Criteria:

Logical clarity Simplicity over over-engineering Real-life applicability Explanation quality (not just code)

Who Should Apply

Students or freshers serious about building a career in

AI / Machine Learning

Candidates who enjoy

logic, experimentation, and problem-solving

Those seeking exposure to

production-style AI systems

What You Will Gain

Hands-on experience building

real matchmaking and recommendation algorithms

Understanding of how AI impacts

human behavior and engagement

Exposure to large-scale consumer AI systems Internship Certificate Potential future opportunities based on performance
Job Types: Full-time, Fresher, Internship
Contract length: 2 months

Benefits:

Food provided
Work Location: In person