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Senior Software Engineer — ML
il y a 1 semaine
Ding’s mission is to enable companies to authenticate their users - at scale - with higher conversion rates and at a lower cost than the market currently offers. Through our platform, customers are able to send OTP codes to their users with the best price to conversion rate ratio available in their current context by selecting the most appropriate channel (SMS via a given carrier vs WhatsApp for example), while preventing costly spam attacks that traditional providers tend to let pass through.
Although Ding was founded a year ago, we already have healthy financials and excellent product-market fit. We have a lot of market-shaking ideas and we look forward to implementing them with you.
We want to build a world-class product and we believe that the way to achieve this is to combine great product design with meticulous engineering. Once we've achieved our goal of providing the best way to send OTP codes to any phone number, we'll be exploring other verticals such as large-scale session management and phone number suggestions, and you could be a key part of these new projects.
The teamThe Ding team started with four former Zenly team members, a social network acquired by Snapchat in 2017. We created Ding in response to a problem we had there: the current SMS authentication offering is too expensive, of poor quality and offers little to no protection against spam. We believe that having a small team of committed and skilled people with a flat hierarchical organization is the best way to build a tech company. It forces us to focus on what matters and only on what matters: delivering value to our customers. We also believe that at our current stage, we need to be face-to-face in our offices in the Tour Montparnasse so that ideas can flow freely and collaboration is spontaneous.
About the RoleAs a machine learning engineer, you’ll design and build Watch, Ding’s next generation anti-fraud machine learning pipeline. Watch will be at the forefront of all public API requests to the OTP API and will filter out suspicious users to fight SMS pumping attacks against customers. We want Watch to be fast, configurable and accurate down to 0.1% false negatives.
As the first ML engineer, your role will be essential in helping to define a culture of excellence in ML, while also contributing to other parts of the backend codebase.
What you will do:- Build and scale an industrial-grade ML system to power Ding’s anti-fraud system.
- Continuously research the latest industry-leading ML solutions and apply them to product ideas.
- Monitor Ding’s traffic to learn about new attack vectors and fraudulent behavior.
- Build and scale a distributed system based on AWS technologies such as DynamoDB, SQS, Redshift and Lambdas, using the Go programming language.
- Develop and maintain internal tools to increase our efficiency and help detect inaccuracies in the machine learning models.
- Work on the next version of the Ding API, which we strive to be industry-leading in terms of usability, performance and functionality.
- You have at least 4+ years of industry experience doing end-to-end ML development on a machine learning team and bringing ML models to production.
- You prefer simple solutions and designs to complex ones, and have a good intuition of what will last a decade and scale.
- You have strong coding skills in Go, Rust, Typescript or any related language for building high performance services.
- You have a product-led mindset: you code for the output, not for the process.
- You are fluent in English.
- Competitive compensation package with BSPCEs.
- Hybrid working style: 3 days at the office and 2 days at home, with 4 weeks of remote per year.
- We will provide you with the gear you need for your role (a laptop and a phone, for on-call rotations).
- An annual offsite in a great location (last one was at Biscarrosse).
- The interview process will start with a 30 min introductory call followed by two technical assessments.
- You will then be invited to our offices at the Tour Montparnasse where we will work together on a small practical project for about an hour, followed by a system design problem and an informal discussion about your past experiences and your working style.
- We don't expect you to ace all these stages, we will evaluate your creativity and how you approach difficult problems. You should consider applying even if you don't meet all the criteria.
