- Position: Machine Learning Engineer
- Experience level: Regular, Senior, Expert
- Salary: 25 000 – 35 000 PLN on B2B (depends on your skills)
Imagine if every time you went to the doctor you paid cash upfront before you are treated, where every time you rented an apartment you paid 2 years cash upfront, or where your monthly paycheck is frequently delayed for months at a time. Then imagine doing that without a loan, credit card, or even bank accounts. This is everyday life for over 3 billion people in developing countries around the world. That is all possible with our client, and they are on a mission to re-invent the way people access and use credit.
Through a simple API integration to the platform, companies can enable their customers to make purchases and pay bills on credit, or get personal loans. Leveraging proprietary datasets, our client builds ML algorithms on customer phone records, bank records, and payment transactions to assess credit risk, enabling us to offer credit lines to individuals and small businesses. This credit line can be used to make purchases from a merchant or withdraw cash without the need for point-of-sale hardware or plastic cards. Because of proprietary data and innovative technical solutions, our client is able to provide credit to underbanked customers who are not typically covered by credit bureaus, a critical area of growth for developing countries.
Responsibilities of the role:
As a machine learning engineer, you will help to productionize findings from data science into real-world applications. Your primary responsibility will be to develop and maintain production-quality software to host machine learning and statistical models, and make them available to the platform. You will also help build model training, publishing, and analytics pipelines for a multitude of machine learning models. In addition, you will validate model integrity and monitor performance while providing data scientists with tooling to accelerate their iteration cycles.
In the first 90 days you would:
- Develop pipelines to compute and store predicted lifetime value for any customer that has changed state recently.
- Build models to predict repayment rates on installment loans given a user’s previous loan history
- Combine credit scores derived from disparate datasets into a single ensemble score that predicts whether a new customer will be a good long-term customer
What we are looking for:
You should be a clear and concise communicator, with an ability to communicate ideas to a wide range of stakeholders both technical and non-technical. You appreciate hearing different points of view and wait to hear other’s point of view before offering your own. You have a pragmatic approach to building systems, see multiple ways of solving problems, and are able to discuss the tradeoffs of each solution. You are technology agnostic with a broad depth and breadth of experience using many different technologies.
Ideally, you have traveled extensively or have lived in a developing country. You are empathetic, self-aware and respect all cultures. You are fun and enlightening to work with, and you have a good work/life balance with hobbies and interests you are happy to share with others.
How we interview and hire:
Our interview process begins with an introductory call to help you better understand the opportunity, give us a glimpse into your interests and motivations, and help you decide if our client is the right place for you to be your happiest and most successful self. From there, we will conduct a technical screen with one of our engineers so you can show us your skills. If all goes well, you will be invited to interview with our data science and software engineering teams. Our interview includes 3-4 technical rounds, as well as conversations around what it is like to work with our client and how you would work with the team on a daily basis. It is designed to assess a broad range of skills so that we can gain a holistic understanding of what you bring to the team and where you shine. We pride ourselves on being transparent throughout the entire interview process with conversations around compensation and the impact you will make.
Why should you work with our client?
When you come to work with our client, you can be assured that your work will be deeply meaningful. You will spend your days solving challenging problems alongside smart and capable colleagues. Daily decisions here have a tangible and immediate impact on millions of people. You will be given both the respect and the latitude to drive best practices for building world-class systems. You will be fully supported by executive management, many of whom have engineering backgrounds and will share your concerns if you say we are accumulating too much technical debt.
One day you will look back and realize that you did some of the best work of your career here. You will have significantly increased your positive impact on the world during your career here. Our client is growing quickly and operating in a trillion-dollar market with few competitors. Your equity has a high chance of producing significant upside.
The tech stack:
- Python for Machine Learning e.g. (Scikit-learn and PyTorch)
- Python for data pipelines
- Scala/Java/Python for micro-services and APIs
- Swagger(OpenAPI) for API documentation
- Docker and Kubernetes to package and run services
- AWS for cloud infrastructure
- On-premise servers for data processing and extraction at our partners
- Degree in a relevant technical field or equivalent experience
- 4+ years of work experience in software engineering
- 2+ years of work experience building and deploying production machine-learned models
- Experience with modern machine learning frameworks like Scikit-learn, Torch or Tensorflow
- Understanding of statistical modeling
- Demonstrable history of building production-quality software infrastructure
- Experience programming in Python and Java
- Experience developing microservices
You will get:
- B2B contract with 26 days of vacation per year
- Unlimited sick leaves
- Internet, headset, education reimbursement
- Latest gear
- Annual bonuses and performance reviews