Apply for Atlassian Off Campus Drive 2025! Hiring Machine Learning Engineer Job in Bangalore for BTech/MTech 2+ years.Work on cutting-edge ML models, LLMs, and AI functionalities across Atlassian products using Python, Spark, Airflow, and cloud tools. Ideal for those looking to build impactful AI systems at scale.
Candidates who are interested in Atlassian Off Campus Drive Job Openings can go through the below to get more information.
Key Job details of Machine Learning Engineer jobs
Company: Atlassian
Qualifications: BTech/MTech
Experience Needed: 2+ years
Job Req ID: REQ-2025-1964
Location: Bangalore
Start Date: 28th July 2025
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Job Description
Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Interviews and onboarding are conducted virtually, a part of being a distributed-first company.
Responsibilities
As a Machine Learning engineer, you will independently work on the development and implementation of the cutting edge machine learning algorithms, training sophisticated models, collaborating with engineering and analytics teams, to build the AI functionality for Atlassian.
Your daily responsibilities will encompass a broad spectrum of tasks such as understanding system and model architectures, conducting rigorous experimentation and model evaluations and dealing with related problems.
Your role is pivotal, stretching beyond these tasks, ensuring AI’s transformative potential is realized across Atlassian products and platforms.
What you’ll do
As an associate Machine Learning engineer, you will work on the development and implementation of the cutting edge machine learning algorithms, training sophisticated models, collaborating with engineering and analytics teams, to build the AI functionality into various tools/platform. Your daily responsibilities will encompass a broad spectrum of tasks such as designing system and model architectures, conducting rigorous experimentation and model evaluations.
Qualifications
On the first day, we’ll expect you to have
Bachelor’s or Master’s degree (preferably a Computer Science degree or equivalent experience).
2+ years of related industry experience in the data science domain.
Familiarity in Python/Java/Golang/Typescript with and the ability to write performant production-quality code, familiarity with SQL, knowledge of Spark and cloud data environments (e.g. AWS, GCloud, Databricks).
Familiarity with LLMs (prompt engineering, RAG), AirFlow, MLFlow, model inferencing, ONNX pipelines will be preferred.
Great verbal and written communication skills along with the ability to explain complex data science and ML concepts to diverse audiences.
Ability to craft compelling stories with data.
Focus on business practicality and the 80/20 rule; very high bar for output quality, but recognize the business benefit of “having something now” vs “perfection sometime in the future”.
Agile development mindset, appreciating the benefit of constant iteration and improvement.
Preference for folks who have worked prior in remote and/or hybrid environments.
Apply Now for Atlassian Machine Learning Engineer Jobs
How to Apply Atlassian Off Campus Drive 2025
Click on Apply to Official Link Atlassian Above – You will go to the Company Official site
First of all Check, Experience Needed, Description and Skills Required Carefully.
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Interview Questions
ML Concepts:
- Explain the bias-variance tradeoff with practical examples.
- How would you evaluate an LLM for summarization tasks?
- Describe RAG pipelines in the context of LLMs.
- Explain feature selection methods and why they matter.
- How would you optimize Spark jobs for a large dataset?
Coding:
- Write Python code to clean and preprocess text for an ML pipeline.
- Implement a basic regression model and evaluate using cross-validation.
- Query optimization in SQL for feature engineering.
System Design:
- Design a scalable ML pipeline for real-time recommendations.
- How would you deploy a model using ONNX and monitor it in production?
- Describe CI/CD best practices for ML model deployment.
Behavioral:
- Describe a time you iterated quickly on a model under business constraints.
- How do you communicate complex ML concepts to a non-technical audience?
- Share your approach to debugging a failing ML pipeline.