Machine Learning Engineer | Funding amount: 400 million yen

【About the Company】

The company develops and manages "AmpliTalk," a sales enablement tool that automates online business negotiations. "AmpliTalk" is a SaaS product that automates coaching and boosts team performance by automatically entering and transcribing online business meetings into SFA for analysis. The company aims to realize "a world where people are empowered through conversations" by using software to streamline and eliminate unnecessary work, and by using software to do what people have been unable to do until now.

【Job Description】 

You will be responsible for the development and operation of ML models related to speech recognition and NLP, as well as back-end development to provide the developed ML models as a service. Rather than researching the latest technologies, our main focus is to provide value to users first and deliver good products as quickly as possible. Therefore, flexibility is required to consider using external ML APIs rather than sticking to in-house development of ML models.
・PoC of ML models
・Research, experimentation, and modification to improve the accuracy of existing models
・Back-end development to serve ML models in the production environment


Language (Machine Learning) : Python
Language (backend) : Python, TypeScript
Machine learning library : scikit-learn, PyTorch
Infrastructure : AWS
Code management : GitHub
CI : GitHub Actions

【Working conditions & treatment】

Flextime System

・Saturday/Sunday/National Holiday
・Annual Paid Leave (3 days when joining the company)
・New Year Holidays

・Full Social Insurance
・Commuting Allowance

【Required】
・2+ years of experience developing ML models
・2+ years of experience in back-end development using ML models
・Japanese: Able to communicate well
・English: No hesitation (as there are many foreign nationals among the engineers)

【Preferred】
・Experience in backend development using TypeScript
・Experience with speech recognition and NLP
・Experience in building MLOps
・Experience with Kaggle