Machine Learning Engineer - Applied AI

Join us.

About the team
The success of 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.

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 pipeline, and enhancing and maintaining the performance of our AI model's predictive automation capabilities.

Responsibilities
What you will do
1. Model optimisation: collaborate with data scientists to improve existing machine learning model training and evaluation pipelines, optimize the model training pipeline speed for faster iteration;
2. Model Deployment: optimize the model inferencing performance through quantization and model conversion, define and leverage appropriate resources for model hosting and inferencing;
3. Inference 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;
4. Service Deployment: build continuous integration, testing, and scalable deployment pipelines in cloud computing environments for machine learning services;
5. 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 model performance;
6. Maintenance: build scalable and reliable infrastructure that supports feature engineering, model training, deployment, inferencing, performance monitoring.

What you will need
1. Ability to understand the business use case to optimise and implement scalable solution;
2. Knowledge of machine learning concepts and fundamentals; deep learning proficiency in at least one of CV and NLP, with solid experience in model training/inferencing optimization such as quantization and conversion;
3. Solid programming skills with experience writing and maintaining high-quality production code;
4. Experience in ML pipeline, model training orchestration; large-scale/distributed training experience is desirable;
5. Ability to work independently and complete projects from beginning to end and in a timely manner;
6. Great communication skills, both written and oral; comfortable presenting findings and recommendations to non-technical audiences.

Qualifications

1. BS or above in Computer Science, Software Engineering, Data Science or a related field;
2. 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;
3. Experience in machine learning frameworks (scikit-learn, Tensorflow, Pytorch), big data frameworks (Spark/Hadoop/Flink) and experience in resource management and task scheduling for large scale distributed systems;
4. Proficient in Python/SQL and one of C++/Go, with deep knowledge of Linux and CD tools (e.g. git); experience with any Go/Python microservice framework is highly desirable;
5. Familiar with cloud infrastructure, good understanding of different data storages and message queues for data streaming and pipelining;

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

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