That sucks. We (Sift Science) have been building something similar for 7 years and aren’t going anywhere. If we can help, please ping me - jason at siftscience dot com
Sift Science | San Francisco, CA | Join the fraud fighting team! https://siftscience.com/ | Onsite
We are a hyper-growth Series C company based in San Francisco. We’re in the business of squashing fraud and other malicious activity for the world’s largest web and mobile businesses. Our next-generation platform is built on proprietary machine learning technology that learns in real time from live activity taking place on the sites and apps of our global network of customers. By integrating our modern REST APIs and automation workflows, our customers not only eliminate risk but also increase revenue and conversion rates through user experience optimization.
Open Roles:
- Data Scientist
- Senior Backend Engineer
- Senior Full Stack Engineer
- Senior Machine Learning Engineer
- Senior Site Reliability Engineer
- Software Mobile SDK Engineer
We’re hiring in our San Francisco and Seattle office and we would love for you to join the team! If interested please visit our careers page and apply https://siftscience.com/careers.
Unfortunately, evil exists. Fortunately, we're here to stop it! Fraud and abuse plague online businesses of all types, from marketplaces to payment processors, social networks to e-commerce stores. As the internet's trust layer, Sift Science's mission is simple yet powerful: make these online experiences faster, smoother, and safer – using the smartest technology around.
Sift Science is hiring for Backend Engineers, SREs, Full Stack Generalists and Mobile SDK Eng.
Curious about what we're working on? Visit us at engineering.siftscience.com to learn more.
CEO and cofounder of Sift Science here. I think we are complementary, actually. Wallarm focuses on security vulnerabilities (like a more automated HackerOne), and we focus more on "application abuse" (user-level fraud).
Unfortunately, evil exists. Fortunately, we're here to stop it! Fraud and abuse plague online businesses of all types, from marketplaces to payment processors, social networks to e-commerce stores. As the internet's trust layer, Sift Science's mission is simple yet powerful: make these online experiences faster, smoother, and safer – using the smartest technology around.
We just raised $30M in Series C funding from Insight Ventures -- join us in making the internet a better place!
Hi Josh, Jason here, CEO of Sift Science. Would love to hear your feedback on what we could do better, whether publicly or privately - jason at siftscience dot com. We want to do better.
Full-time. Sift Science uses real-time machine learning to fight online fraud. It's a problem that cost U.S. merchants > $12B last year with 70% being a result of organized crime. We are currently seeking ML engineers to join our team to work on our diverse and exponentially growing dataset to employ large-scale, online machine learning and model millions of unique features. Sift is a tight-knit team that likes board games, yummy food, and solving challenging technical problems. Check out https://siftscience.com/jobs or ping us at [email protected] for more information :)
Sift Science (YC S11) | San Francisco | Full time | Onsite
Sift Science uses real-time machine learning to prevent and predict online fraud. We are a lean group of driven and collaborative people passionate about bringing machine learning into the real world. Do you despise evil? Believe fraud must be eradicated from the internet? If so, Sift Science must be for you.
We are hiring for engineers (front-end, machine learning and everything in between), sales, marketing and business experts.
Visit jobs.siftscience.com or reach out directly to [email protected] for details/questions.
Sift Science (http://siftscience.com) CEO here. We focus on helping online businesses like Airbnb, Match.com, and OpenTable automate away their fraud problems with realtime machine learning. Many of our customers use us an additional layer of protection in addition to what Stripe or any other payment gateway offers. I'm happy to answer any questions about fraud.
Hi Mark, CEO of Sift Science here. Thanks for giving us a shot. Please don't hesitate to ping me if you need any help or we're not delivering to your expectations. jason at siftscience dot com
Sift Science, San Francisco, is Hiring Machine Learning Engineers
Full-time, Onsite. Sift Science uses real-time machine learning to fight online fraud. It's a problem that cost U.S. merchants > $12B last year with 70% being a result of organized crime. We are currently seeking ML engineers to join our team to work on our diverse and exponentially growing dataset to employ large-scale, online machine learning and model millions of unique features. Sift is a tight-knit team that likes board games, yummy food, and solving challenging technical problems. Check out https://siftscience.com/jobs or ping us at [email protected] for more information :)
Sift Science (YCS11) is Hiring Machine Learning Experts
San Francisco, Full-time, https://siftscience.com
Sift Science uses real-time machine learning to fight online fraud. It's a problem that cost U.S. merchants > $12B last year with 70% being a result of organized crime. We are currently seeking ML engineers to join our team to work on our diverse and exponentially growing dataset to employ large-scale, online machine learning and model millions of unique features. Sift is a tight-knit team that likes board games, yummy food, and solving challenging technical problems. Check out https://siftscience.com/jobs or ping us at [email protected] for more information :)
Jason here, op and CEO of Sift Science. You have a point, but do keep in mind that the TV is going to a bad customer -- one that won't reward Best Buy with repeat business (perhaps just more fraud) and won't spread positive word of mouth (except to let other fraudsters know that Best Buy is a great fraud target). So there is some "lose" in shipping the TV to a bad customer, different from shipping it to a good customer. Does that make sense?