Software Engineer - Machine Learning, Trust and Safety

Mercari India; Bengaluru; Hybrid; INR 1,200,000 - 1,800,000; Full time

Job summary

Mercari India in Bengaluru is seeking an experienced Machine Learning Engineer to lead end-to-end development of ML systems. You will collaborate with cross-functional teams to design solutions, conduct data analysis, and enhance trust and safety algorithms.

The ideal candidate has over 5 years of experience in developing large-scale ML systems and is proficient in using frameworks like TensorFlow and PyTorch. This role embraces a hybrid workstyle, offering flexibility between the office and home.

Qualifications

• 5+ years of experience in ML systems development.
• Strong background in developing and deploying ML solutions.
• Experience with machine learning frameworks.

Responsibilities

• Collaborate with teams to design and implement solutions.
• Conduct data analysis to enhance algorithms.
• Monitor system performance and run A/B testing.

Skills

Machine learning solutions

Cloud platforms (AWS, GCP, Azure)

Data analysis

Kubernetes

Docker

Tools

TensorFlow

PyTorch

scikit-learn

NumPy

pandas

Job description

Work Responsibilities

Mercari is actively applying advanced machine learning technology to provide a more convenient, safer, and more enjoyable marketplace. Machine learning engineers use the cloud and Kubernetes to operate and improve machine learning systems.

Bold Challenges

• We are looking for people who are interested in our services, mission, and values, and want to work where engineers can go bold, use the latest technology, make autonomous decisions, and take on challenges at a rapid pace
• Collaborate with cross-functional teams and product stakeholders to gather requirements, design solutions, and implement features that improve user engagement
• Conduct data analysis and experimentation with large-scale data sets to identify patterns, trends, and insights that drive the refinement of trust and Safety algorithms
• Utilize machine learning frameworks and libraries to deploy scalable and efficient content moderation, fraud and financial security solutions
• Monitor system performance and conduct A/B testing to evaluate the effectiveness of features
• Continuously research and stay updated on advancements in AI/machine learning techniques and recommend innovative approaches to enhance the trust and safety capabilities

Requirements

Minimum Requirements

• Over 5+ years of professional experience in end-to-end development of large-scale ML systems in production
• Strong experience demonstrating development and delivery of end-to-end machine learning solutions starting from experimentation to deploying models, including backend engineering and MLOps, in large scale production systems
• Experience using common machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, NumPy, pandas)
• Deep understanding of machine learning and software engineering fundamentals
• Basic knowledge and skills related to monitoring system, logging, and common operations in production environment
• Communication skills to carry out projects in collaboration with multiple teams and stakeholders

Preferred Skills

• Experience developing AI-based anomaly detection and content moderation systems
• Functional development and bug fixing skills necessary to improve system performance and reliability
• Experience with Docker and Kubernetes
• Experience with cloud platforms (AWS, GCP, Microsoft Azure, etc.)
• Microservice development and operation experience with Docker and Kubernetes
• Utilizing deep learning models/LLMs in production
• Experience in publications at top-tier peer-reviewed conferences or journals

Employment Status

Full-time

Office

Bangalore

Hybrid workstyle

We believe in high performance and professionalism. We work from office for 2 days/week and work from home 3 days/week. To build a strong & highly-engaged organization in India, we highly encourage everyone to work from our Bangalore office, especially during the initial office setup phase. We will continue to review and update the policy to address future organizational needs.

Work Hours

• Full flextime (no core time)
• Flexible to choose working hours other than team common meetings