Software Engineer-Machine Learning Infrastructure

at  ByteDance

About ByteDance
Founded in 2012, ByteDance’s mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Helo, and Resso, as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.
Why Join Us
Creation is the core of ByteDance’s purpose. Our products are built to help imaginations thrive. This is doubly true of the teams that make our innovations possible.
Together, we inspire creativity and enrich life - a mission we aim towards achieving every day.
To us, every challenge, no matter how ambiguous, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always.
At ByteDance, we create together and grow together. That’s how we drive impact - for ourselves, our company, and the users we serve.
Join us.
About the Team
Our team plays a crucial role in ensuring the company’s success. We seek people who are willing to learn and put in the effort to solve problems. Our challenges are not your regular day-to-day problems - you’ll be part of a team that’s developing new solutions to new challenges. It’s working fast, at scale, and we’re making a difference. We are looking for talents to join us on this exciting journey!

Responsibilities

• Design and implement a global-scale machine learning (ML) system for feeds, ads, and search ranking models.
• Improve use-ability and flexibility of the ML infrastructure.
• Improve the workflow of model training and serving, data pipelines, and resource management for the multitenancy ML systems.
• Optimize performance of ML system.
• Design and implement tools to analyze the ML model’s performance.

Must have a Master’s degree in Computer Science, Engineering (any), Information Technology, Data Science, Data Analytics, Statistics, Mathematics, or a related quantitative field, and 1 year of related work experience; OR a Bachelor’s degree in Computer Science, Engineering (any), Information Technology, Data Science, Data Analytics, Statistics, Mathematics, or a
related quantitative field, and 3 years of related work experience.

Of the required experience, must have 1 year of experience in each of the following:

• Coding using Python or C++;
• Optimizing, training, and deploying machine learning and deep learning models using PyTorch or TensorFlow;
• Developing code and identifying issues in Linux environment;
• Developing and deploying large-scale machine learning systems; and
• Utilizing Big data framework using MySQL, Spark, Hadoop, or Flink.

Type: Full time, 40 hours/week
Location: San Jose, CA
Salary Range: $184662 - $250000 per year
To Apply, click the apply button below. Contact lpresumes@bytedance.com if you have difficulty submitting resume through the website.
ByteDance Inc. 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 Bytedance, 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.
ByteDance Inc. is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at lpresumes@bytedance.com.

• Design and implement a global-scale machine learning (ML) system for feeds, ads, and search ranking models.
• Improve use-ability and flexibility of the ML infrastructure.
• Improve the workflow of model training and serving, data pipelines, and resource management for the multitenancy ML systems.
• Optimize performance of ML system.
• Design and implement tools to analyze the ML model’s performance