About The Team
The success of the data business model hinges on the supply of a large volume of high quality labeled data that will grow exponentially as our business scales up. However, the current cost of data labeling is excessively high. The Data Solutions team is built to understand data strategically at scale for all Global Business Solution (GBS) business needs. Data Solutions Team uses quantitative and qualitative data to guide and uncover insights, turning our findings into real products to power exponential growth. Data Solutions Team responsibility includes infrastructure construction, recognition capabilities management, global labeling delivery management.
About The Role
We are looking for a highly capable machine learning engineer to deploy and optimise our machine learning systems. You will be evaluating existing machine learning (ML) lifecycle, understanding and productionizing the model data pipeline, and enhancing and maintaining the performance of our AI model's predictive automation capabilities.
Responsibilities
- Model optimisation: Collaborate with data scientists to improve existing machine learning model training and evaluation pipelines, updating/finetuning the models with different training resources such as GPU or distributed training
- Model Deployment: Build continuous integration, testing, and scalable deployment pipelines in cloud computing environments for machine learning services
- Data pipeline productionisation: Work with data scientists and data engineers to design and implement the data pipelines for machine learning models that will support the current and future needs of our business
- Maintenance: Build scalable and reliable infrastructure that supports feature engineering, model training, deployment, inferencing, performance monitoring
- Tracking: Build logging, tracking, analyzing, monitoring and reporting pipelines for both data and model tracking in cloud computing environments to ensure correct model output and stable model performance
Requirements
- Ability to understand the business use case to optimise and implement scalable solution
- Knowledge of machine learning concepts and fundamentals
- Deep learning proficiency in at least one of CV and NLP, with solid experience in model finetuning and optimization
- Solid programming skills with experience writing and maintaining high-quality production code
- Experience in ML pipeline, model training orchestration; large-scale/distributed training experience is desirable
- Ability to work independently and complete projects from beginning to end and in a timely manner
- Great communication skills, both written and oral; comfortable presenting findings and recommendations to non-technical audiences
Qualifications
- BS or above in Computer Science, Software Engineering, or a related field
- 3+ years of industry experience building ML infrastructure at scale
- At least 1 year of experience in developing and deploying large-scale systems, version control, scaling and monitoring
- Experience in machine learning frameworks (scikit-learn, Tensorflow, Pytorch), big data frameworks (e.g., Spark/Hadoop/Flink) and experience in resource management and task scheduling for large scale distributed systems.
- Proficient in Python/SQL and one of C/C++/Go, with deep knowledge of Linux and CD tools (e.g. git); Experience with any microservice framework is highly desirable
- Knowledge of machine learning concepts and fundamentals
- Good communication and teamwork skills to clearly communicate technical concepts with other teammates.