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Tim Tully, Partner at Menlo Ventures, grew up in Silicon Valley, where a love for coding was kindled in him. Tim is a technologist to the core, which innately led him to become an elite technical leader at companies like Splunk and Yahoo.
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At a hyper-growth startup, a solution from six months ago will unfortunately no longer scale. The business is growing rapidly, and this traffic to this service in particular was growing at an unprecedented rate. We hit a point where it needed re-architecting to support 10x the current scale.
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At Abnormal Security, we use a data science-based approach to keep our customers safe from the most advanced email attacks. This requires processing huge amounts of data to train machine learning models, build datasets, and otherwise model the typical behavior of the organizations we’re protecting.
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Tony Dong, Director of Engineering at Rippling, is no stranger to the diverse set of engineering problems that fast-growing startups create. Before building and leading his teams at Rippling, Tony was CTO and co-founder at PerShop, a YC backed startup, and Senior Engineer at Twitter, Periscope, and TellApart.
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As Abnormal grows, we have to maintain a scalable codebase across all areas of engineering to prevent issues around testing, maintainability, and documentation. When organizations scale, a common problem is that the codebase becomes cluttered, with multiple teams writing different code that accomplishes the same task.
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Many companies aspire to be customer-centric, but few find a way to operationalize customer-centricity into their team’s culture. As a 3x SaaS startup founder, most recently at Orum, and a veteran of Facebook and Palantir, Ayush Sood...
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Working at hyper-growth startups usually means that unreasonable expectations will be thrust on individuals and teams. Demanding timelines, goals, and expectations can lead to high pressure, stress, accountability, and ultimately, extraordinary growth and achievements.
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In engineering teams, there’s a mythical concept of a “10x engineer”— engineers who have 10x more impact and responsibility than the average engineer. Do these engineers actually exist? Is this a myth, or a possibility that engineers can realistically aim to become?
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Over the last three years building our ML-based cybersecurity products at Abnormal Security, I’ve benefitted enormously from discussions with colleagues in the ML space. This podcast aims to make some of those conversations available. In our second episode of Abnormal Engineering Stories...
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It’s one thing to add machine learning and artificial intelligence features to an existing software platform. It’s quite another to build an entire company like Abnormal Security around machine learning technology, and to provide practical, everyday value to enterprise organizations.
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As VP of Engineering here at Abnormal Security, I’ve had numerous conversations with our team, venture capitalists, and external engineering leaders about the challenges of building and leading engineering teams. Building applied machine learning products at scale requires solving a wide range of challenges...
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Our ML pipeline powers a detection engine that catches the most advanced email attacks. These attacks are not only extremely rare, but also change over time in an adversarial way. Since we require both high precision and high recall, and the cost of any error is severe, it is essential...
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Machine learning engineering is hard, especially when developing products at high velocity, as is the case for us at Abnormal Security. Typical software engineering lifecycles often fail when developing ML systems.
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Developing a machine learning product for cybersecurity comes with unique challenges. For a bit of background, Abnormal Security’s products prevent email attacks—think credential phishing, business email compromise, and malware—and also...
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In a recent post, our Head of Platform & Infrastructure Michael Kralka discussed how Abnormal’s rapid growth has forced us to make our core services horizontally scalable. In-memory datasets that start off small become huge memory...
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Abnormal Security has a data-driven culture that permeates the entire organization, from the engineering team to product, sales, and beyond. We make decisions by analyzing data monitoring relevant metrics. For online analytics, we use a great tool called Grafana...
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Blog blue circles algorithm
At the core of all Abnormal’s detection products sits a sophisticated web of prediction models. For any of these models to function, we need deep and thoughtfully engineered features, careful modeling of sub-problems, and the ability to join data from a set of databases. For example, one type of email attack...
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At Abnormal, the problems we are trying to solve are not that much different from those being tackled by other organizations, including large enterprises. What is unique to startups are the additional constraints placed on the solution space, such as the amount...
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On October 21st, 2020, just two weeks before the US general election, many voters in Florida received threatening emails purportedly from the “Proud Boys." These attacks often included some personal information like an address or phone number, threatened violence...
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At Abnormal Security, one of our key objectives is to build a detection engine that can continuously adapt to a changing attack landscape. As such, we want to ensure that our systems can rapidly adjust to recent and high-value messages—even with...
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Sophisticated social engineering email attacks are on the rise and getting more advanced every day. They prey on the trust we put in our business tools and social networks, especially when a message appears to be from someone on our contact list, or even...
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Jesh Bratman, a founding member at Abnormal Security and Head of Machine Learning, was just featured on The Tech Trek’s podcast. Jesh deeps-dives into his past, building ML systems to detect abusive behavior at Twitter, and how he used this background to transition...
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