Software Engineer - Machine Learning, Trust and Safety in India at Jobgether
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Job Description
This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Software Engineer - Machine Learning, Trust and Safety in India.
This role sits at the intersection of machine learning, large-scale systems engineering, and platform trust, focusing on building intelligent solutions that make online marketplaces safer, more reliable, and more user-friendly. You will work on production-grade ML systems that power fraud detection, content moderation, and risk prevention across a global platform. The environment is highly collaborative and fast-moving, bringing together engineers, product teams, and data stakeholders to solve complex, real-world problems at scale. You will contribute across the full ML lifecycle, from experimentation and model development to deployment and optimization in production. The role emphasizes ownership, innovation, and continuous improvement in a cloud-native, Kubernetes-driven infrastructure. It is ideal for engineers who enjoy solving ambiguous problems and applying ML to high-impact trust and safety challenges.
Design, build, and maintain end-to-end machine learning systems that support trust and safety use cases such as fraud detection, content moderation, and platform security across large-scale production environments.
- Collaborate with cross-functional teams to define requirements, design ML-driven solutions, and deliver features that enhance user trust and engagement
- Develop, train, deploy, and optimize machine learning models using modern frameworks such as TensorFlow or PyTorch
- Conduct large-scale data analysis and experimentation to uncover patterns, improve algorithms, and refine model performance
- Build and operate scalable ML pipelines using cloud infrastructure, Kubernetes, and MLOps best practices
- Monitor system performance, run A/B tests, and continuously improve models based on production feedback
- Stay current with advancements in AI/ML and apply innovative approaches to strengthen trust and safety systems
This role requires strong experience in building and deploying large-scale machine learning systems in production, combined with solid software engineering and cloud-native development skills.
- 5+ years of experience in end-to-end ML system development, including experimentation, model deployment, and production operations
- Strong proficiency in machine learning frameworks such as TensorFlow or PyTorch, and libraries like scikit-learn, NumPy, and pandas
- Deep understanding of machine learning principles, software engineering fundamentals, and system design
- Experience with MLOps practices, including monitoring, logging, and maintaining production ML systems
- Hands-on experience with cloud platforms (AWS, GCP, or Azure), Docker, and Kubernetes in microservices or distributed environments
- Strong communication skills with the ability to collaborate across engineering, product, and data teams
- Preferred: experience in anomaly detection, content moderation systems, LLMs in production, or research publications in ML/AI
- Full flextime policy with autonomy over working hours
- Opportunity to work on large-scale, high-impact ML systems in a global tech environment
- Collaborative, innovation-driven engineering culture with strong ownership and autonomy
- Exposure to cutting-edge AI/ML technologies, including production-scale deep learning systems
- Professional growth opportunities within a rapidly expanding global engineering center
- Inclusive and values-driven workplace focused on diversity, trust, and well-being