JD #1938
Applications open
Internship
LinkedIn Recruitment Associate
Fresh Prints
About the role
Fresh Prints is a fastβgrowing American startβup that began as a customβapparel brand and quickly pivoted to become a global player in the printβonβdemand space. Founded in 2016, the company now employs over 400 professionals across three countries and prides itself on a "Day 1" mindset β constantly iterating, learning and scaling. The culture is built around ownership, agility and a strong sense of community, which helped the team navigate the COVIDβ19 crisis by shifting to PPE production and emerging stronger. Fresh Prints continues to expand its footprint in the United States, Europe and Asia, and is now looking for energetic individuals in India to support its talent acquisition engine.
The role of LinkedIn Recruitment Associate is designed for fresh graduates or interns who want to dive straight into the world of recruiting without getting bogged down by repetitive admin tasks. You will work remotely from anywhere in India, collaborating with the global recruiting team to source Campus Managers for universities across the United States. The position offers a handsβon learning environment where you will be exposed to modern sourcing techniques, AIβdriven recruiting tools such as Claude, and realβtime hiring projects that directly impact the companyβs growth.
Key responsibilities include:
1. Researching U.S. universities to identify highβpotential talent pools.
2. Sourcing candidates on LinkedIn and other platforms using Boolean search strings.
3. Building and nurturing talent pipelines through personalized outreach.
4. Screening applicants based on graduation year, field of study and role criteria.
5. Maintaining accurate candidate data in the ATS.
6. Collaborating with the hiring manager to refine sourcing strategies.
7. Experimenting with AI recruiting tools to improve efficiency.
8. Contributing ideas to streamline the hiring workflow.
9. Preparing weekly reports on sourcing metrics.
10. Participating in mentorship sessions with senior recruiters.
The tech stack for this role is lightweight but modern β LinkedIn Recruiter, Google Workspace, AI assistants (Claude), and a cloudβbased ATS. Successful performance can lead to a fullβtime offer as a Recruiter or Talent Acquisition Specialist, providing a clear growth path within Fresh Printsβ global HR function. Joining Fresh Prints means you will gain mentorship from seasoned recruiters, work on highβimpact projects, and be part of a culture that rewards curiosity, proactiveness and a willingness to experiment. The stipend, health insurance and exposure to AI tools make this internship a valuable stepping stone for anyone aspiring to build a career in talent acquisition or HR technology.
Eligibility & process
Who can apply
Bachelor's degree in any discipline (Engineering, Management, Arts, Science etc.) with minimum 60% aggregate or CGPA 6.0; fresh graduates or candidates with at least one internship in recruiting, HR, sales or marketing; no active backlogs; strong command of written and spoken English; willingness to work nightβshift hours (5β―PMβ1β―AM IST).
Skills & stack
Selection process
- Round 1: Online aptitude/logic test
- Round 2: Sourcing case study or live LinkedIn search exercise
- Round 3: HR interview focusing on cultural fit and motivation
Prep kit
18 resourcesStudy material 3
Aptitude and Reasoning Practice
Helps you prepare for the online assessment by covering quantitative, logical and verbal aptitude questions.
Company Interview Preparation Guide
Provides insights into common interview formats and sample questions useful for recruitment and HR roles.
Comprehensive Interview Prep Resource
Covers a wide range of interview topics, including behavioral and technical questions for freshers.
Company-specific guides 4
APTITUDE & LOGIC
Aptitude Practice Questions & Online Mock Tests
Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.
COMPANY GUIDE
Fresh Prints Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Fresh Prints and off-campus tech roles.
MOCK TESTS & PAPERS
Comprehensive Interview Prep Resources & Syllabus
Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.
CODING PRACTICE
Algorithm and Data Structure Problem Set
Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (LinkedIn sourcing, Boolean search, English communication, research, talent pipeline building, data entry, AI tool familiarity, organization, selfβmotivation, adaptability) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Fresh Prints Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Fresh Prints as a LinkedIn Recruitment Associate?
Highlight Fresh Prints's market reputation, recent tech innovations, and how your skills in LinkedIn sourcing directly solve their team's objectives.
Highlight Fresh Prints's market reputation, recent tech innovations, and how your skills in LinkedIn sourcing directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
$450 per month (including $370 stipend + $80 nightβshift allowance)
Internship
Remote, India
Nov 04, 2026
