Autify, Inc. is a start-up company founded in San Francisco by a team of engineers who were the first Japanese team to graduate from the top US accelerator Alchemist Accelerator.
With the mission to increase creativity with the power of tech, we develop and provide Autify, an AI-based software test automation platform.
Since its official launch in October 2019, over 300 companies have implemented Autify for Web, a test automation platform for web applications. The product is steadily growing, and we have provided basic functions expected in a test automation platform. However, we are receiving many requests for additional features and improvements from our clients. We also launched the beta-version of Autify for Mobile, a test automation platform for mobile apps, in April 2021, and we continue to expand our product lineup.
Internationally minded engineers
Almost half of our engineering team is non-Japanese, and we’re all fluent English speakers, so we don’t require any Japanese ability. We currently have ten engineers, including the CTO and a designer.
We’re open to people living abroad looking to relocate to Japan to join us, so long as you’re a Japanese citizen or eligible for a visa to work as a software engineer. You may work as a subcontractor until you obtain a visa. We’re also open to people who don’t want to relocate to Japan, provided you’re willing to work during Japanese business hours.
Two week sprints and lots of automated testing
Our development process kicks off by creating a ticket describing what kind of feature we’ll develop. In some cases, the product owner creates it based on user feedback, and in other cases, each team creates bugs and investigation requests directly.
We’re rooted in Scrum, and our development cycle consists of a two-week sprint. Development tasks are assigned during the planning stage on the first day of the sprint, and we hold a daily standup meeting to check the overall progress and any problems.
We review code on GitHub. After the review, the merged branches are automatically deployed to the staging environment, and E2E tests are performed (which Autify does itself).
After testing on the integrated branch, it’s deployed to the production environment. Deployment is done as needed, usually several times a week. Developers themselves will demo what they’ve worked on.
As you can imagine, having a technical product aimed as developers means engineering drives our company. Some ways this manifests itself are:
- Tech is selected at each member’s discretion
- We’re active adopting new tech
- We actively work to pay off technical debt we create
- There is a learning support allowance for technical skill improvement
- We’ll lend you a PC with your desired specs, external monitors, etc.
We’re working completely remotely. We do have face-to-face all-hands meetings once per quarter, where we rent a space in Tokyo for everyone to get together. Because of this, there’s no need for you to live in Tokyo, though we would prefer you live somewhere in Japan (once a visa can be arranged).
Ruby on Rails and Go on the backend, TypeScript and React on the frontend
- Backend: Ruby, Ruby on Rails, Go
- Machine learning: Python
- Database: PostgreSQL, Redis
- Test: Jest, Selenium, WebdriverIO, Puppeteer
- Infrastructure: AWS, Docker, Terraform, Packer
- Monitoring: New Relic, Sentry, Papertrail
- CI/CD: CircleCI
- Source code management: GitHub
- Project management: ClickUp
- Misc.: G Suite, Slack, Notion, Discord, Zoom, 1Password, Mixmax, Calendly, etc.
Our response to COVID-19
With COVID-19, we moved from working remotely one day per week to working completly remotely. We used to have an office, but cancelled it in June 2020, to completely shift our work style to remote work. Once COVID-19 subsides, we plan to host monthly meetups, but otherwise continue to completely work remotely.
See our company deck for more background on our business, culture, and hiring process.
About the position
We are looking for an expert in machine learning to help us build AI systems to deliver value to our customers. You will work on designing, developing, training, and deploying medium to large-scale deep learning models.
The ideal candidate will be passionate about artificial intelligence and stay up-to-date with the latest developments in the field.
You will primarily work with images (screenshots of web pages and mobile apps) and text. Your work will be mostly related to Computer Vision and Natural Language Processing. Most of the projects are developed in a supervised fashion, some semi-supervised and some in reinforcement learning settings.
- Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
- Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
- Writing models from scratch and using off-the-shelf models for solving a given problem.
- Optimizing model for best performance vs. accuracy trade-off
- Writing cost-optimized and long-running training pipelines for medium to large models
- Designing metrics to estimate the model quality
- Developing pipelines for automatically deployment models to production
- 2+ years of experience as a machine/deep learning engineer
- Bachelor’s degree in computer science, data science, mathematics, or a related field
- Advanced proficiency in writing code using Python
- Extensive knowledge of using Deep Learning Frameworks (preferably PyTorch)
- Comfortable in using a remote Linux server as a primary development environment
Nice to haves
These aren’t required, but be sure to mention them in your application if you have them.
- Master’s degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or related technical field
- Experience handling both research and development.
- Experience working with Containers, Docker, and Kubernetes
Desired Personality Traits
- Someone who is passionate about Machine Learning and stays up-to-date with the latest developments in the field
- A person who understands the building blocks of ML and is able to translate a business problem into an ML problem
¥8.5 to 12 million annually.
- Coding test (take-home)
- Technical assignment (take-home)
- Technical interview - 90min
- Final interview - 90min