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.

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