Swiggy is hiring a Machine Learning Engineer (0-1 yrs exp)

Position:

• Title: Machine Learning Engineer - I (DSP)

Company:

• Name: Swiggy

Location:

• City: Bangalore
• State: Karnataka
• Country: India

Job Type:

• Type: Full-time

Job Mode:

• Mode: Remote

Job Requisition ID:

• ID: Not specified

Years of Experience:

• Required Experience: 0-1 years

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Company Description

• Swiggy is one of India's largest and leading on-demand delivery platforms.
• The company adopts a tech-first approach, focusing on logistics and developing solutions to meet consumer demands.
• Swiggy operates in 500 cities across India, partnering with hundreds of thousands of restaurants.
• It has a large employee base of over 5000 employees and a 2 lakh+ strong fleet of independent delivery executives.
• The company leverages cutting-edge machine learning (ML) technology and processes terabytes of data every day to offer seamless and fast delivery experiences to millions of customers.
• Since its inception in 2014, Swiggy has grown from a hyperlocal food delivery service to a logistics hub of excellence, known for its reliable services.
• Recent expansions include Swiggy Instamart, Swiggy Genie, and Guiltfree, adding more diversity to Swiggy's offerings.
• Innovation and employee growth are key, with the company providing a productive and fulfilling work environment.

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Profile Overview

• Title: Machine Learning Engineer I (DSP)
• The Machine Learning Engineer will join Swiggy’s highly dynamic engineering team and contribute significantly to the development of the company’s ML-driven solutions.
• The ideal candidate should be familiar with ML fundamentals, algorithms, and concepts of object-oriented programming (OOPs).
• Candidates should have proficiency in Spark, Python programming, and ETL pipelines.
• The role will also require knowledge of cloud provider stacks such as AWS, Azure, or GCP.
• Expertise in Kubernetes, Scala programming, and libraries like TensorFlow and PyTorch is highly desired.
• The engineer will collaborate with the data science team to ensure robust and scalable decision-making processes.
• The role involves building, deploying, and maintaining ML models across platforms, using both in-house and AWS infrastructure.

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Qualifications

• ML Fundamentals and Algorithms: Strong understanding of key machine learning concepts and principles.
• Programming Languages: Proficiency in Python, Spark, and SQL programming. Scala programming knowledge is also advantageous.
• API Integration: Experience in integrating APIs with various services and platforms.
• Cloud Platforms: Knowledge of cloud technologies such as AWS, GCP, or Azure.
• ETL Pipelines: Understanding of Extract, Transform, Load (ETL) pipelines to handle large data sets.
• Kubernetes: Familiarity with container orchestration using Kubernetes.
• ML Frameworks: Experience with libraries like TensorFlow, PyTorch, and ONXX.
• Additional Skills: Knowledge of tools like Langchain is a plus.
• Experience Level: 0-1 years, ideally with an understanding of data mining strategies and advanced ML techniques.
• Collaboration: Ability to work alongside cross-functional teams, including data scientists and engineers.

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Additional Info

• Platform Development: The role requires deploying machine learning models on multiple platforms, including Swiggy’s in-house ML platform and AWS.

• Team Collaboration: Engineers will collaborate with the data science team to optimize decision-making processes, focusing on scale, latency, and throughput.

• Tools Development: The engineer will contribute to creating tools and systems to accelerate the ML lifecycle.

• Algorithm Adoption: The position plays a significant role in encouraging the use of sophisticated algorithms and innovative data mining strategies.

• Data Products: Engineers will also contribute to Swiggy’s various data-driven products and services.

• Tech Blog: Explore Swiggy’s tech blog for more insights into their challenges and solutions:

  • Swiggy Delivery Challenge
  • AI Transforming Convenience
  • Deploying Deep Learning Models

• Work Culture: Swiggy fosters an inclusive work environment. The company offers equal employment opportunities to all candidates regardless of race, color, religion, gender, or any other legally protected status.

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Detailed Breakdown of Responsibilities

Building and Deploying ML Models:

• Deployment on Various Platforms: The candidate will work on deploying machine learning models on diverse platforms, integrating services from other engineering teams.
• In-house ML Platform: Swiggy has its own machine learning platform, and the engineer will play a key role in its development and usage.
• Cloud Integration: You will utilize cloud services like AWS to support the company’s large-scale model deployment.
• Seamless Integration: Work with internal and external teams to seamlessly integrate machine learning models with core business operations.

Collaboration with Data Science Team:

• Team Collaboration: The role emphasizes collaborating with Swiggy's data science team to drive impactful decision-making across different departments.
• Optimization: Engineers must think about optimizing the system for scale, latency, and throughput—vital aspects that drive operational efficiency.
• ML Lifecycle Management: The Machine Learning Engineer will focus on accelerating the ML lifecycle by developing new tools and systems that ensure smoother processes.

Contribution to Data-Related Products:

• Development of Data Products: The engineer will contribute to various data-driven products that Swiggy develops, ensuring they’re both scalable and optimized for performance.
• Algorithmic Adoption: The engineer is expected to lead efforts in enabling sophisticated algorithmic strategies for decision-making and data mining.
• Enhancement of Data Services: The role involves improving Swiggy's data services by adding advanced ML-driven features that improve user experience.

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Core Technical Skills and Technologies:

ML Fundamentals and Programming:

• Python Programming: Advanced skills in Python programming are essential, especially in the context of machine learning.
• Spark: Experience with Spark is a critical requirement for handling large-scale data processing.
• SQL: Proficiency in SQL is essential for querying and managing Swiggy's vast datasets.
• Cloud Provider Stack: The engineer must be comfortable working with cloud services such as AWS, GCP, or Azure.
• Kubernetes: Experience in container orchestration with Kubernetes is necessary for scalable model deployment.

Advanced ML Libraries and Tools:

• TensorFlow and PyTorch: Familiarity with these libraries will be crucial for developing machine learning models.
• ONNX: Understanding ONNX format for machine learning models is highly desirable.
• Langchain: While not mandatory, knowledge of Langchain would be an added advantage.

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Equal Opportunity Employment Statement:

• Inclusivity: Swiggy is an equal opportunity employer, and all qualified candidates will receive consideration without discrimination based on legally protected attributes.
• Commitment to Diversity: The company values diversity and promotes a work culture of inclusiveness and respect for everyone.

Please click here to apply.

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