Machine Learning Engineer | Food and Ingredients EC Company

【ABOUT THE COMPANY】

Sale of safe foods and food ingredients, such as organic vegetables, specially cultivated agricultural products, and additive-free processed foods, to the general public through websites and catalogs.

In 2017, two companies merged their businesses, and in 2018, another company was added to create a group. The company is taking a leap forward in a sustainable society by providing a service that allows many people to enjoy a better diet and by creating a system that rewards and makes producers of good food proud.

The company's main business area is a value-propositioned food e-commerce business, such as meal kits that meet the needs of busy customers, such as organic and specially cultivated fresh food, processed food with minimal additives, and other products that are safe and secure. In addition, they are solving social issues related to food through our business, such as wholesale of foodstuffs to nursery schools, consulting and media business using our e-commerce know-how, and mobile supermarkets that serve as infrastructure for living in depopulated areas.

Now that the company has entered its second decade of business, it will continue to provide a better dietary life for producers and customers through new initiatives and tricks, such as strengthening the products and services of each brand, establishing a new large-scale distribution center, and building a logistics system.

【JOB DESCRIPTION 】

■Vision / Aim
Continuing to create a sustainable food future
Under the corporate philosophy of "Future Dining, Future Farms," the company aims to realize a sustainable society by constantly evolving methods to connect people who eat and produce food.

For the company, solving social issues related to food through business methods is the mission and value it provides. Therefore, each member's contribution to the business performance and strong sense of ownership leads to the growth of the entire business, which in turn leads to the solution of social issues. In this environment, members of the company work on their missions while always synchronizing their personal growth with their contributions to society.

Through further expansion of the business, the company will create a better society, a sustainable society, and create and expand the future of food.

■Team Mission
"Using Data to Solve Social Issues Related to Food and Grow the Business"

The company is currently implementing a DX project under the growth strategy of "providing food that is good for the earth and people through the power of business models and technology," and as part of this project, the company is rebuilding its information utilization infrastructure and utilizing data based on this infrastructure.
To leverage this foundation to promote data utilization across the company, the company has established a Data Management Office (DMO), which brings together data governors, data engineers, data scientists, ML engineers, and others.
The DMO has two roles: one is to promote data and BI governance to promote data utilization throughout the company, and the other is to solve business unit issues using machine learning and other technologies.
The company's full-scale use of data has just begun, and there are many issues to be solved. However, there is such great room for growth that the company is looking for members who have the passion and enthusiasm to create their own data utilization organization, realize a data-driven company, solve food issues, and leave a big impact on society.

■Responsibilities
The company is using data to solve social issues in food using data science and other technologies to solve business problems. The Data Management Office (DMO) will be responsible for the end to end development, operation, and implementation of machine learning models, as the Data Management Office (DMO) is responsible for the use of challenging technologies such as machine learning and optimization across the company. The Machine Learning Engineer will lead the development and implementation of machine learning models based on engineering best practices.

■Attractiveness of this position
・Involved in testing and implementing the latest technologies.
They are just now starting to select a machine learning architecture and are in the phase of selecting one.
They are also looking for those who are willing to consider whether to focus on SageMaker or to look more broadly at OOS, SaaS tools, etc.
For the ML pipeline, should we use Airflow, which we use on the data engineering side, or something specialized for machine learning like Kubeflow, or should we stick with AWS and use SageMaker Pipelines?
For monitoring, do we use SageMaker Model Monitor or implement a GUI-based tool like Aporia?
In this position, you will have the opportunity to gain extensive experience with the latest technologies, including their selection, construction, operation, and management.

・Involved in the launch of a new organization
They are currently in the process of launching the Data Management Office (DMO) as a start-up organization that will promote company-wide data utilization across the entire company. As the first machine learning engineer in the organization that will realize company-wide data utilization, you will lead the data science organization of the team that will grow in the future.

・An environment where you can focus on your work output and develop your technical skills
Remote work is available, so you can choose where you can concentrate most. In fact, there are members who work from the office of a subsidiary in Kyushu, and many engineers have not been to the office for several months. Also, with no core time, you can focus on producing the output you are looking for.
Secondary jobs are allowed, and by getting involved in projects at other companies and gaining diverse experience, you can develop your skills as an engineer. In addition, study sessions on data science and MLOps are held within the company, providing an environment where employees can learn and teach each other knowledge.

■Key Job Responsibilities
Develop machine learning pipelines/models/production code
Implement engineering best practices into the machine learning model development and operation process
Select machine learning system architecture
Collaborate with business unit personnel, data scientists, data engineers, and other stakeholders to define requirements

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【Welfare】
Full social insurance
Commuting allowance
Medical checkup
Remote work and shortened hours on request
　※Many mommy and daddy employees also work there.
Sidelines OK
Clothing optional
Employee discounts are available
Two days off per week (Saturday and Sunday)
※Depending on the situation, you may be required to work either Saturday or Sunday, but you will be asked to take a day off.
There is a space for napping
Free space (free to use for lunch and rest)

【Holidays】
Saturday/Sunday/National Holiday
Annual Paid Leave
New Year Holiday
Parental Leave
Nursing Care Leave
Congratulations & Condolence Leave

【Required】
2+ years of experience in developing, implementing, and implementing machine learning models in production
3+ years of experience in developing and operating production-level applications in Python
Experience in agile development for MLOps and DevOps
Basic knowledge of machine learning pre-processing, algorithms, etc.
Strong communication skills to ensure smooth communication with related departments
Self-motivation and curiosity to research and implement new technologies that have never been used before
Ability to read English well enough to follow the latest MLOps trends
Ability to make appropriate decisions on whether or not to implement technologies, taking into account both the impact of the technology and business needs
Japanese: Business level~ (JLPT N2~)

【Preferred】
Deep knowledge of machine learning pre-processing, algorithms, etc.
Experience in building and operating a machine learning infrastructure that uses OSS and SaaS tools to develop, manage, and operate machine learning models
Experience in implementing and operating MLOps tools (SaaS, OSS)
　-ML pipeline tools such as Kale, Luigi, etc.
　-Model monitoring tools such as Aporia, Evidently AI, etc.
　-Model serving such as Kserve, Seldon Core, TorchServe, etc.
　-Model management with DVC, etc.
　-Platforms including all of the above such as Vertex AI, SageMaker, Kubeflow, etc.
Experience in listening to business staff and proposing solutions to their problems.
Experience in business implementation and routine operation of machine learning models under your own initiative
Experience developing using cloud technologies (AWS, GCP)
Experience winning prizes in data science competitions such as Kaggle
Experience in competitive programming competitions such as AtCoder
Empathy with the company's mission of solving food challenges