Kaushik is a technical leader at Meta, and has over 10 years of experience building AI-driven products at companies like LinkedIn and Google. Shalvi is an AI scientist at SAP, and has experience as a data scientist, a software engineer, and project manager. Frank is a founding engineer at co:rise and started his career at Coursera, where he was the first engineering hire and built much of the platform's original core infrastructure. The following excerpts from Jake's conversation with Kaushik, Shalvi, and Frank have been edited and condensed for clarity. You can watch the complete recording here. Kaushik, you've been a hiring manager at some big companies. You get a lot of resumes. What are you looking for? What advice do you have for someone who's working on their resume and thinking about how to position themselves? Kaushik: In terms of skills, I'm looking for a practical knowledge of applying ML to build products. That's something I think you can't get from books -- you have to have some hands-on experience. I'm not necessarily looking for someone to have experience with specific tools or techniques, because those things are constantly changing. It's more that I want to know about the approach they took. Why did they use the tools they did, and what did they do when things got tricky or didn't work the first time? Don't get me wrong, I think having a good theoretical foundation is definitely necessary. But I would say you should spend as much time as you can solving real problems. That's how you learn which techniques work best for which use cases, and it will help you get a better understanding of the theoretical side, too. Kaushik: In terms of preparing for interviews, other than brushing up on the fundamentals, my advice would be to brainstorm a couple of problems that are relevant to the company you're interviewing with and do some background research on the common techniques to solve those problems.
Facebook is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.Facebook is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at .
As general chair of this year's ACM Conference on Knowledge Discovery and Data Mining (KDD), Huzefa Rangwala, a senior manager at the Amazon Machine Learning Solutions Lab, has a broad view of the topics under discussion there. Two of the most prominent, he says, are graph neural networks and fairness in AI. Graphs are data representations that can encode relationships between different data items, and graph neural networks are machine learning models that are useful for knowledge discovery because they can be used to infer graph structures. "Our world is connected in lots of ways, so you'll see graph neural networks find applications in lots of different domains, all the way from social networks and transportation networks to knowledge graphs and drug discovery," Rangwala says. The Amazon Machine Learning Solutions Lab brings the expertise of Amazon scientists and the resources of Amazon Web Services to bear on customers' machine learning problems.
The impact of artificial intelligence in business is rising day by day. You presumably engage with artificial intelligence (AI) regularly without even realizing it. There are a lot of use cases for artificial intelligence in everyday life. It is even more than you can imagine. Are you scared of AI jargon? We have already created a detailed AI glossary for the most commonly used artificial intelligence terms and explained the basics of artificial intelligence as well as the risks and benefits of artificial intelligence for organizations and others. Although the acceptance of AI in modern society is recent, the idea is not.
The machine learning operations (MLOps) product category has been moving quickly, especially in the last year, and several platforms have emerged to take it on. Cloud providers including AWS and Microsoft, analytics players including Databricks and Cloudera, MLOps pure plays like Algorithmia, and even open source projects like MLflow, offer integrated platforms to manage machine learning model experimentation, deployment, monitoring and explainability. Now Spell, a New York City-based MLOps startup, is providing an MLOps platform specifically geared to deep learning. As such, Spell refers to its platform, announced last week, as facilitating "DLOps." ZDNet spoke with Spell's head of marketing, Tim Negris as well as its CEO and co-founder, Serkan Piantino (who previously served as Director of Engineering at Facebook AI Research, and who opened Facebook's New York City office).
Nightfall AI, a startup providing cloud data loss prevention services, today announced that it raised $40 million in Series B financing from investors including WestBridge Capital, Venrock, Bain Capital Ventures and -- for some reason -- athletes and celebrities including Paul Rudd, Drew Brees and Josh Childress. CEO Isaac Madan says that the proceeds will be put toward doubling Nightfall's 60-person headcount, scaling the platform to more customers and markets, and expanding Nightfall's partner ecosystem. Isaac was previously a VC investor at Venrock, where he focused on early-stage investments in software as a service, security and machine learning. Rohan was one of the founding engineers at Uber Eats, where he designed and built software to grow the platform's footprint. Madan says he and Sathe were inspired to launch Nightfall by Sathe's personal experiences with data breaches arising from poor "data security hygiene."
Machine learning and AI continue to reach further into IT services and complement applications developed by software engineers. IT teams need to sharpen their machine learning skills if they want to keep up. Cloud computing services support an array of functionality needed to build and deploy AI and machine learning applications. In many ways, AI systems are managed much like other software that IT pros are familiar with in the cloud. But just because someone can deploy an application, that does not necessarily mean they can successfully deploy a machine learning model.
Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Low-code and no-code artificial intelligence (AI) tools and immersive 3D visualization are heralding the next age of ecommerce. As one of the world's largest selling platforms, eBay has leveraged AI for some time now -- but it has typically been done behind the scenes for recommendation systems, fraud detection and predictions of customer intent, explained Stephanie Moyerman, former senior director of risk and trust science at eBay. "What we want to do is integrate [AI] as part of the natural buying and selling experiences and flows," she told viewers during a live stream at this week's Transform 2022 event.
Role requiring'No experience data provided' months of experience in Columbus We are a rapidly growing AI Machine Learning Software Start-up with secured funding looking for a 100% remote Senior Python Software Engineer. It's important that you have extensive cloud infrastructure experience and building robust APIs within the Python Flask framework. Important influence and Input on the product you're helping build We are looking for a seasoned Senior Cloud Engineer to help implement Kubernetes and help create ETL pipelines at scale (Airflow).
Originally published in Snapchat Engineering, July 11, 2022. Snapchat ad ranking aims to serve the right ad to the right user at the right time. These are selected from millions of ads in our inventory at any time. We do so with a strong emphasis on maintaining an excellent user experience and upholding Snap's strong privacy principles and security standards, including honoring user privacy choices. Serving the right ad, in turn, generates value for our community of advertisers and Snapchatters.