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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