Senior Machine Learning Engineer, Adtima (Ads Recommendation)

Adtima is the leading advertising platform in Vietnam's Zalo ecosystem, matching ads to more than 80M users in real time and serving hundreds of millions of ad impressions every day.

We are looking for a Senior Machine Learning Engineer to own the production side of our ads recommendation system. Our models retrain continuously and serve in real time; your mission is to make sure the whole ML lifecycle - data, features, training, deployment, and serving - runs smoothly, reliably, and transparently.

You will build the platform and standards that let our Data Scientists ship new models quickly and safely, working closely with Backend and Data Engineering.

This role is focused on the reliability and performance of the ML system. Model development and experimentation are owned by our Data Scientists, so you can concentrate on making the ML platform production-grade.

What you will do

ML Lifecycle Reliability

• Own the pipelines that continuously train, validate, and deploy models, so only models that meet quality standards reach production - and can be rolled back safely when they don't
• Make every production model versioned, traceable, and reproducible, and set the engineering standards that let new models move from experiment to production smoothly.

Observability

• Build end-to-end visibility into data, features, models, and serving, so issues are detected and resolved before they impact the business
• Lead ML-related incident response and drive root-cause fixes that prevent recurrence.

Serving Performance

• Partner with Backend to keep model serving fast, stable, and cost-efficient as traffic and model complexity grow.

Feature Platform

• Collaborate with Data Science, Data Engineering, and Backend on the design and rollout of a centralized feature store, so features are defined once and served consistently across training and inference.

What you need

Technical Skills

• 3+ years in ML engineering, MLOps, or backend/data engineering supporting ML systems in production
• Strong Python and software engineering practices - testing, code review, Git workflows, and CI/CD
• Operating ML pipelines and containerized workloads (Airflow or similar; Docker, Kubernetes)
• Managing the model lifecycle in production - deployment and serving (ONNX, TorchServe, Triton, or similar), model registry and versioning (MLflow or similar)
• Monitoring, observability, and data validation for production systems (Prometheus, Grafana, or similar)
• Working knowledge of deep learning (PyTorch) and large-scale data processing (Spark, SQL, or similar)
• Strong debugging and systems problem-solving - able to investigate failures across data pipelines, training, deployment, and inference.

Soft Skills

• Strong ownership and reliability mindset
• Communicates clearly across Data Science, Backend, and Data Engineering.

Nice-to-Have Qualifications

• Experience with feature stores (Feast, Tecton, Hopsworks) or building centralized feature pipelines
• Java or Scala - our online serving runs on the JVM
• Streaming and low-latency stores: Kafka, Redis
• Familiarity with ClickHouse or other OLAP databases
• GPU inference optimization, vector search, or approximate nearest neighbor retrieval
• Ad tech or recommendation system domain experience
• An AI-first mindset - using AI/LLM tools to automate operations and accelerate engineering.

Why Join Adtima?

• Large-scale ML challenges: run real-time ML systems for an advertising ecosystem serving over 80 million users and hundreds of millions of ad impressions daily
• Technical and business exposure: hands-on experience across ML infrastructure, model serving, data pipelines, and advertising systems
• Meaningful impact: the reliability and performance of the ML system directly protect ad revenue and advertiser trust
• End-to-end ownership: shape the ML platform - from feature store to deployment and monitoring - with a high degree of autonomy
• AI-powered workflow: company-provided AI accounts (Claude) to boost your productivity in engineering and operations
• Large-scale ML challenges: run real-time ML systems for an advertising ecosystem serving over 80 million users and hundreds of millions of ad impressions daily
• Technical and business exposure: hands-on experience across ML infrastructure, model serving, data pipelines, and advertising systems
• Meaningful impact: the reliability and performance of the ML system directly protect ad revenue and advertiser trust
• End-to-end ownership: shape the ML platform - from feature store to deployment and monitoring - with a high degree of autonomy
• AI-powered workflow: company-provided AI accounts (Claude) to boost your productivity in engineering and operations.