Senior Machine Learning Engineer

About us

Populix is a consumer insights platform that helps businesses connect with its database of respondents and provides them with insights to better understand the preferences of Indonesian consumers. Populix has a pool of over 1,000,000 diverse, readily accessible, and highly qualified respondents across Indonesia. Its products range from intensive research studies to simple surveys and can be arranged on a project or subscription basis. Focusing on Indonesian consumers being super sticky to their phones, Populix facilitates a diverse range of data collection methods via its mobile app.

About the Role

Populix is building the future of AI-powered market research, combining structured data, unstructured insights, and generative AI into a seamless research intelligence platform. We're looking for a Senior Machine Learning Engineer to accelerate that vision. Someone who can design, build, and optimize production-grade ML systems that turn research challenges into scalable, high-impact solutions.

In this role, you’ll be at the core of deploying advanced ML models into production, from retrieval-augmented generation (RAG) systems to agentic AI workflows and automation pipelines. You’ll work closely with data scientists, backend engineers, and product teams to translate research prototypes into robust, high-performance services that power Populix’s next-generation research platform.

You’ll also play a key role in evolving our ML infrastructure: scaling pipelines for text, audio, and survey data, improving efficiency through distributed systems and event-driven architecture, and ensuring reliability through monitoring, CI/CD, and cloud-native practices. This is a chance to not only build impactful systems but also influence the technical strategy behind how AI is operationalized at scale.

Key Responsibilities

• Design, develop, and optimize APIs (using FastAPI) to serve both application logic and machine learning models at scale.
• Research, prototype, and implement cutting-edge methods such as Retrieval-Augmented Generation (RAG) to integrate knowledge sources with large language models (LLMs).
• Explore, benchmark, and evaluate methodologies to improve system performance, reliability, and user experience.
• Work closely with data scientists, backend engineers, data engineer, product managers, and frontend developers to translate business and product requirements into robust ML-driven solutions.
• Deploy and monitor ML models in production environments, ensuring high availability, scalability, and efficiency.
• Contribute to system design involving event-driven architecture (Celery, Arq, Google Pub/Sub, or other message brokers) and ensure seamless integration with APIs, databases, and cloud environments.
• Implement and maintain comprehensive unit tests and integration tests to ensure code quality, system reliability, and smooth deployment.

Required Qualifications

• 4+ years of professional experience as a Machine Learning Engineer or related experience.
• Strong proficiency in Python, with experience in frameworks such as scikit-learn, PyTorch, Hugging Face, LangGraph, LangFuse, or equivalents.
• Hands-on experience with RAG architectures and knowledge integration techniques with LLMs.
• Proficiency in FastAPI, APIs, SQL, NoSQL, and data management best practices.
• Solid experience with Google Cloud Platform (GCP), Docker, and CI/CD pipelines.
• Practical experience with Celery, Arq, Google Pub/Sub, or other message brokers.
• Proven experience deploying ML models into production systems.

Preferred Qualifications

• Familiarity with Golang or Rust for performance-critical components.
• Demonstrated ability to optimize large-scale ML systems.
• Strong understanding of system design and distributed systems.