Ml Engineer

1 day ago

Singapore BETTERDATA PTE. LTD. Full time

**Who are we looking for**:
We seek an experienced **Machine Learning Engineer (Senior)**to transform cutting-edge research into robust, production-ready services for synthetic data generation and to optimize both deep learning and classical ML algorithms (e.g. tree-based models) at enterprise scale (billions of rows). You will build and tune model pipelines end-to-end, ensuring high performance, scalability, and reliability across diverse workloads and dataset sizes.

**Key Responsibilities**:
**Algorithm Optimization & Scaling**
- Optimize bottlenecks of the deep generative models to accelerate training and generation of generative models (e.g. transformer, diffusion, GANs).
- Implement distributed training of the models across multi-GPU clusters.
- Optimize distributed training of traditional ML models (e.g. XGBoost, LightGBM, CatBoost) on billion-row datasets.
- Design best practices for memory management to maximize resource utilization (compute and memory), enabling faster training at lower cost.

**Data Handling at Scale**
- Collaborate with data engineers to design ETL/ELT workflows handling terabyte to petabyte scale tabular and unstructured data.
- Implement scalable feature engineering pipelines using distributed computing frameworks (e.g. Spark, Dask, or Ray).
- Automate data validation (e.g. schema checks, anomaly detection) with rule-based and ML-driven frameworks.

**End to end orchestration**
- Build ML pipelines that transition research prototypes into reliable production-grade workflow.
- Package models into Docker containers and deploy using Kubernetes.
- **Build automated model and data quality monitoring and validation systems**to ensure data integrity throughout the pipeline lifecycle.
- Design robust **error handling**mechanisms, with **automatic retries**and **data recovery**in case of pipeline failures.
- Implement logging, monitoring and alerting systems.

**Qualifications**:

- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Software Engineering, Data Science or a related quantitative discipline.
- 5+ years of hands-on experience optimizing and scaling machine learning models in production environments.
- Demonstrated track record of accelerating model training workflows (e.g., transformers, diffusion models, GANs) at multi-GPU scale.
- Experience in operating ETL/ELT pipelines handling terabytes to petabytes of tabular and unstructured data using distributed computing tools (e.g. Apache Spark, Dask, Ray).
- Demonstrated ability to translate research prototypes into reliable, production-grade ML pipelines with rigorous testing and validation.
- Experience in the ML orchestration (e.g. airflow, dagster).

**Good to have**
- Experience hosting models to scalable cloud infrastructure (AWS / Azure / GCP).
- Experience containerisation of the data pipelines & AI models in docker with supporting orchestration tools (e.g. kubernetes).

**Benefits**:

- Flexible time-off arrangements
- Flexible work arrangements - work from office at One North or WFH on some days

**How to apply**:
Does this role sound like a good fit to you?

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