Machine Learning Engineer
Jakarta, ID
- Pay
- Pay not listed
- Work setting
On-site — work setting source
Work setup in the employer’s listing: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s listing: Full-Time
From the employer’s posting
What you’ll work on
Full postingBuild ETL pipelines that turn raw data into model features
Build a platform to automate ML training, testing and maintenance
Turn data scientists' prototype models into well integrated, high-performance systems
Make data science code maintainable, scalable and debuggable
From the employer’s posting
ETL (Extract, Transform and Load) of raw data into model features
building platform to automate ML training, testing and maintenance
transform prototypes of predictive models into high performance, well integrated systems
make sure that data science code is maintainable, scalable and debuggable
What you’ll bring
All qualificationsCore experience
- 2-3 years of relevant experience in Data or Software Engineering and minimum 1-2 years hands-on experience in deploying machine learning models and feature pipelines
- Ability to write robust production-ready code in Python & GoLang
- Proficiency in machine learning libraries such as: Tensorflow/Keras, Pytorch, SparkML or MLeap
- Familiarity using a distributed computing platform, such as Hadoop or Spark
Qualification wording
2-3 years of relevant experience in Data or Software Engineering and minimum 1-2 years hands-on experience in deploying machine learning models and feature pipelines
Ability to write robust production-ready code in Python & GoLang
Proficiency in machine learning libraries such as: Tensorflow/Keras, Pytorch, SparkML or MLeap
Familiarity using a distributed computing platform, such as Hadoop or Spark
Tools in this posting
- Go
- Python
- SQL
- AWS
- Hadoop
- MySQL
- PostgreSQL
- S3
- Spark
- Keras
- PyTorch
- TensorFlow
Source — Tool mentions in context
- Having good knowledge of data structures, data modeling and software architecture related to Machine Learning - Ability to write robust production-ready code in Python & GoLang - High degree of proficiency with RDBMS (i.e. MySQL, PostgreSQL) and No-SQL platforms (i.e. S3)
- Ability to write robust production-ready code in Python & GoLang - High degree of proficiency with RDBMS (i.e. MySQL, PostgreSQL) and No-SQL platforms (i.e. S3) - Proficiency in machine learning libraries such as: Tensorflow/Keras, Pytorch, SparkML or MLeap
- Strong interest in building scalable and reliable Machine Learning Engineering/AI related services - Past experience working with cloud services platform to build data pipelines, monitoring, scheduler and storage, preferably on AWS environment. - Familiarity using a distributed computing platform, such as Hadoop or Spark
- Past experience working with cloud services platform to build data pipelines, monitoring, scheduler and storage, preferably on AWS environment. - Familiarity using a distributed computing platform, such as Hadoop or Spark Thank you for your interest in Kredivo. We’re thrilled that you’d like to join our company. Unfortunately, days are short and applicants are many, so we won’t have time to meet everyone. If you are among qualified candidates, you will receive a call or email from our Talent Acquisition team.
- High degree of proficiency with RDBMS (i.e. MySQL, PostgreSQL) and No-SQL platforms (i.e. S3) - Proficiency in machine learning libraries such as: Tensorflow/Keras, Pytorch, SparkML or MLeap - Strong interest in building scalable and reliable Machine Learning Engineering/AI related services
Posting record
Report an errorOpen 4 monthsWe first saw this listing 142 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 3, 2026 · 142 days ago
- Recorded sightings
- 181
- Last seen by us
- Oct 23, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
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Job description
At Kredivo we are building the future of financial services that are fast, cheap and widely accessible, wrapped up in a beautiful UX. As we are growing bigger, we are looking for Machine Learning Engineer who will closely collaborate with Data Science team in feeding data (across functions such as, credit scoring, fraud, and risk) into financial models defined by data scientists. You will also be responsible to make sure that data science code is maintainable, scalable and debuggable.
Job Responsibility
- Develop and implement ML pipeline infrastructure, including but not limited to: ETL (Extract, Transform and Load) of raw data into model features and building platform to automate ML training, testing and maintenance.
- Partnering with Data Scientists to transform prototypes of predictive models into high performance, well integrated systems.
Qualifications
- 2-3 years of relevant experience in Data or Software Engineering and minimum 1-2 years hands-on experience in deploying machine learning models and feature pipelines
- Having good knowledge of data structures, data modeling and software architecture related to Machine Learning
- Ability to write robust production-ready code in Python & GoLang
- High degree of proficiency with RDBMS (i.e. MySQL, PostgreSQL) and No-SQL platforms (i.e. S3)
- Proficiency in machine learning libraries such as: Tensorflow/Keras, Pytorch, SparkML or MLeap
- Strong interest in building scalable and reliable Machine Learning Engineering/AI related services
- Past experience working with cloud services platform to build data pipelines, monitoring, scheduler and storage, preferably on AWS environment.
- Familiarity using a distributed computing platform, such as Hadoop or Spark
Thank you for your interest in Kredivo. We’re thrilled that you’d like to join our company. Unfortunately, days are short and applicants are many, so we won’t have time to meet everyone. If you are among qualified candidates, you will receive a call or email from our Talent Acquisition team.
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Source details
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- Pay
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- Location & working pattern
Jakarta, ID
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