SpyneInternshipsOfficial Job Posting

SDE Intern | Freshers (0–1 Year) at Spyne

Gurugram, HaryanaOnsiteFreshers / 0–1 YearsNot specified

Spyne is an AI-first automotive retail company redefining how cars are marketed and sold using Generative AI. Backed by $16M in Series A funding from leading investors including Vertex Ventures and Accel, Spyne is scaling rapidly across global markets such as the US and EU.

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Spyne SDE Intern | Freshers (0–1 Year) career opportunity

Official Role Details

Role Overview

About Spyne

Role Overview

As a Software Development Engineer (SDE) Intern, you will join the Technology team for a hands-on 6-month engineering internship in Gurugram. You will collaborate with Frontend, Backend, and AI teams to build, debug, and scale real-world products.

Candidates who perform exceptionally well during the internship will be eligible for a full-time role (PPO).

Applicant Requirements

Eligibility Criteria & Qualifications

Degree / QualificationB.E / B.Tech / MCA / B.Sc

Eligible BatchesNot specified

Official Eligibility Points

Pursuing or recently completed B.Tech / B.E. / BS / MS in Computer Science or related fields.
Freshers or candidates with 0–1 year of relevant experience; immediate joiners preferred.
Strong preference for students/graduates from tier-1 engineering institutes (IITs, NITs, BITS, DTU, NSUT).

Day-to-Day Deliverables

Key Responsibilities

Model Development & Experimentation

•Build and improve frontend and backend features using technologies like React.js and Node.js.
•Debug application issues, perform root cause analysis, and optimize performance.

Data Pipelines & Feature Engineering

•Develop and integrate REST APIs, backend services, and manage data-driven applications.
•Participate in code reviews and collaborate with cross-functional AI, product, and engineering teams.

Evaluation, Deployment & Optimization

•Convert UI/UX requirements into functional, scalable software features.

Technical Qualifications

Required Skills & Technologies

JavaScriptReact.jsNode.jsData Structures & AlgorithmsREST APIsDatabasesGit/GitHub

Official Application Method

How to Apply

Submit your application directly through Spyne's verified careers portal.

Review the published role requirements, degree qualifications, and eligible graduation batches above.

Ensure your resume highlights relevant projects and contains working GitHub repository links.

Click the official application button below to open Spyne's direct careers page.

Complete and submit your application directly on the employer's portal.

Apply on Spyne Official Careers Page

You will be directed to Spyne's official careers portal. Career Insights does not process applications or charge any candidate fees.

The specifications above are verified official details published by Spyne. Candidates should verify all final terms, deadlines, and eligibility directly on Spyne's official careers portal before submitting an application.

Career Insights

Recommended Preparation

Independent editorial guidance based on the published role information.

What to Prepare

Evaluate your machine learning background against published criteria and modeling preparation focus areas.

Education & Academic Criteria

Cohort: Eligible: Not specifiedPublished Requirement

Published Requirement

B.E / B.Tech / MCA / B.Sc

Preparation Focus

Hiring evaluations prioritize demonstrable problem-solving, domain competence, and practical project evidence over specific degree pedigree.

Machine Learning & Python

Published Requirement

Applied machine learning, statistical modeling, and Python libraries.

Preparation Focus

Revise ML evaluation metrics (Precision, Recall, F1, ROC-AUC), feature engineering, loss functions, and model deployment via REST endpoints (FastAPI).

Data Pipelines & Model Training

Published Requirement

Model development, dataset preprocessing, and experimentation.

Preparation Focus

Understand dataset splits, regularization techniques to prevent overfitting, vector embeddings, and modular code hygiene in ML pipelines.

Data Storage & Persistence

Published Requirement

Familiarity with databases, schema modeling, and structured query writing.

Preparation Focus

Be prepared to explain relational normalization (1NF-3NF), indexing trade-offs, foreign key constraints, and transactional consistency.

Version Control & Engineering Hygiene

Published Requirement

Familiarity with Git branching, pull requests, and collaborative repository management.

Preparation Focus

Review Git branch workflows, pull request code reviews, and maintain clean commit history with descriptive messages.

Work Mode & Collaboration

Published Requirement

Onsite (Gurugram, Haryana) operational structure.

Preparation Focus

Highlight proactive written communication, technical documentation discipline, and the ability to troubleshoot errors systematically before escalating.

Technical Topics

Depending on the hiring team’s evaluation style, technical assessments for this role typically cover these core areas:

Topic 1: Python & Machine Learning Fundamentals

Recommended Preparation

Python data science libraries (NumPy, Pandas, PyTorch / TensorFlow)
Supervised and unsupervised ML algorithms, loss functions, and optimizers (SGD, Adam)
Feature normalization, embeddings, train/validation split discipline, and preventing data leakage

Preparation Tip: Be ready to explain how you select appropriate baseline algorithms and write modular, testable Python code without data leakage.

Topic 2: NLP, LLM APIs & Transformer Concepts

Recommended Preparation

Tokenization, embeddings, attention mechanisms, and Transformer architectures
Prompt engineering, structured output extraction, and LLM API integration
Retrieval-Augmented Generation (RAG) and semantic search workflows

Preparation Tip: Demonstrate understanding of tokenization nuances, context window limits, and how LLM outputs can be validated against structured schemas.

Topic 3: Document Understanding & Computer Vision

Recommended Preparation

Document layout analysis, optical character recognition (OCR) pipelines, and bounding-box validation
Table extraction, key-value pair parsing, and multimodal vision-language concepts
Handling image noise, scan distortions, and irregular document structures

Preparation Tip: Be prepared to explain document parsing workflows: differentiating layout detection, text OCR, and relationship extraction on complex documents.

Topic 4: Evaluation Methodology, Metrics & Error Diagnostics

Recommended Preparation

Precision, Recall, F1-Score, ROUGE, BLEU, and exact match metrics
Curating rigorous test sets and designing stratified validation splits
Error analysis: categorizing model failure modes, confusion matrices, and verifiable AI

Preparation Tip: Emphasize evaluation discipline: why aggregate accuracy is insufficient and how you curate challenge sets to diagnose specific model vulnerabilities.

Topic 5: Experimentation, Research Documentation & Reproducibility

Recommended Preparation

Systematic experiment tracking (MLflow, Weights & Biases) and ablation study design
Documenting research hypotheses, benchmarks, and reproducible pipelines
Communicating model confidence thresholds, error taxonomy, and verifiable AI considerations

Preparation Tip: Highlight scientific rigor: explain how you structure experiments so that any team member can reproduce your training runs and benchmark results.

* Note: These are independent Career Insights editorial recommendations to help candidates structure their study plan. They do not represent guaranteed interview stages or official hiring mandates from Spyne.

Practical Project

Demonstrate the machine-learning workflow by building a small project that covers data preparation, modeling, evaluation, and reproducible results.

Practical Project

Document Intelligence & Agentic Table Extraction Pipeline

AI/ML Research Project

What to Build

A reproducible document intelligence pipeline in Python that extracts structured tabular data from complex, multi-page PDF documents or scanned invoices using layout-aware models or LLM-assisted verification, complete with automated benchmark scoring.

Skills Demonstrated

Python, PyTorch / Transformers, Document AI & OCR, Model Evaluation & Error Diagnostics, Test-Set Curation, Reproducible Research Documentation.

GitHub Deliverables

Clean repository with environment setup (requirements.txt), sample evaluation dataset, benchmark scripts with precision/recall scoring, error analysis notebooks, and clear architecture documentation.

Interview Talking Points

How you designed your evaluation test set, failure mode taxonomy (how the model handles rotated scans or dense tables), and the trade-offs between heuristic rules vs deep learning models.

Career Insights recommendation: This project is an independent editorial recommendation designed to help candidates build verifiable portfolio evidence. It is not required or sponsored by Spyne.

Before You Apply

Verify your model notebooks, evaluation summaries, and application materials before applying.

1. Role-Tailored Resume & GitHub Links

Ensure your resume clearly highlights hands-on projects involving JavaScript, React.js, Node.js. Include verified, publicly accessible GitHub repository links with clean README files.

2. Eligibility & Batch Verification

Confirm that your graduation year (Not specified), educational background, and location match the employer's published requirements before applying.

3. Standout Project Code & Architecture Review

Have 1–2 key projects prepared for technical discussion. Be ready to explain your database schema choices, API contracts, error boundaries, and design trade-offs.

4. Direct Submission on Spyne's Official Portal

Submit your application directly through Spyne's official careers portal using the verified apply link below. Career Insights does not process applications or charge any candidate fees.

Career Growth Context

Career Insights editorial perspective on typical milestones and research contribution cycles in applied ML internships.

Research Orientation & Baseline Benchmarking

Familiarizing oneself with the team’s research tools, dataset pipelines, and core model baselines under technical guidance.

Hands-on Experimentation & Error Diagnostics

Conducting exploratory modeling experiments, benchmarking against baselines, and performing rigorous error analysis on complex inputs.

Validation, Reproducibility & Research Documentation

Documenting experimental methodology, optimizing inference trade-offs, and validating agentic or document intelligence workflows.

Performance-Based Conversion Opportunity

Demonstrating consistent research rigor, problem-solving, and technical ownership. As noted in available employer postings, full-time conversion represents an opportunity evaluated based on individual performance and organizational needs rather than a guaranteed progression.

Career Insights Context: Career Insights Editorial Perspective: Development pathways described here reflect general industry research patterns. Specific team workflows, mentorship structures, and conversion evaluations vary by organization. Infrrd’s published posting noted an opportunity for full-time conversion based on performance.

Career Insights Editorial Standards

This page provides verified job details sourced from Spyne's recruitment announcement, alongside independent preparation roadmaps authored by the Career Insights editorial team. Career Insights is not affiliated with Spyne and does not collect candidate data or charge application fees.

Role Overview

About Spyne

Role Overview

Eligibility Criteria & Qualifications

Key Responsibilities

Model Development & Experimentation

Data Pipelines & Feature Engineering

Evaluation, Deployment & Optimization

Required Skills & Technologies

How to Apply

Recommended Preparation

What to Prepare

Technical Topics

Topic 1: Python & Machine Learning Fundamentals

Topic 2: NLP, LLM APIs & Transformer Concepts

Topic 3: Document Understanding & Computer Vision

Topic 4: Evaluation Methodology, Metrics & Error Diagnostics

Topic 5: Experimentation, Research Documentation & Reproducibility

Practical Project

Document Intelligence & Agentic Table Extraction Pipeline

Before You Apply

Career Growth Context

Research Orientation & Baseline Benchmarking

Hands-on Experimentation & Error Diagnostics

Validation, Reproducibility & Research Documentation

Performance-Based Conversion Opportunity

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