To start building an ML Platform, you should support the basic ML user journey of notebook prototyping to scaled training to online serving. If your organization has multiple teams, you may additionally need to support administrative requirements of multi-user support with identity-based authentication and authorization. Two popular OSS projects – Kubeflow and Ray – together can support these needs. Kubeflow provides the multi-user environment and interactive notebook management. Ray orchestrates distributed computing workloads across the entire ML lifecycle, including training and serving.

We're Cruise, a self-driving service designed for the cities we love. We're building the world's most advanced self-driving vehicles to safely connect people to the places, things, and experiences they care about. We believe self-driving vehicles will help save lives, reshape cities, give back time in transit, and restore freedom of movement for many. In our cars, you're free to be yourself. We're creating a culture that values the experiences and contributions of all of the unique individuals who collectively make up Cruise, so that every employee can do their best work.

Brex empowers the next generation of businesses with an integrated corporate card and spend management software. We make it easy for our customers to manage every aspect of spending and empower their employees to make better financial decisions from anywhere they live or work. Brex proudly serves tens of thousands of growing businesses, from early-stage startups to enterprise leaders. Working at Brex allows you to push your limits, challenge the status quo, and collaborate with some of the brightest minds in the industry. We're committed to building a diverse team and inclusive culture and believe your potential should only be limited by how big you can dream.

Our team researches and develops machine learning models and systems that help make the Internet a safer place. We serve customers across multiple verticals such as online commerce, delivery service, finance, travel sites, etc., and we have customers in both developed and developing countries. Our technology helps protect Internet users from ever-evolving online scams, payment fraud, abusive content, account takeover, etc. We are a forward-thinking team constantly challenging ourselves and the status quo to push the boundary of machine learning and data science across multiple product offerings at Sift and collaborate with product engineering teams to deliver tangible customer value. We take pride in our work, not ourselves.

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Grammarly is excited to offer a remote-first hybrid working model, which combines the flexibility of working from home with the benefits of gathering in person. Team members can work primarily remotely in the United States, Canada, Ukraine, Germany, Poland, and Portugal. Conditions permitting, teams will meet in person a few times every quarter at one of Grammarly's hubs, currently in San Francisco, Kyiv, New York, Vancouver, and Berlin, or in a shared workspace in Krakow. Grammarly team members who will be collaborating in Berlin must be based in Germany, Ukraine, Poland, or Portugal. Grammarly empowers people to thrive and connect, whenever and wherever they communicate.

The Data and Machine Learning Platforms team paves the path for any Wayfair team to make informed decisions leveraging data and ML. This team builds and maintains multiple solutions for streaming data, analytics, ML model development and training, a feature store, centralized model registry, and a scalable model deployment platform. Wayfair is one of the world's largest online destinations for the home. Whether you work in our global headquarters in Boston or Berlin, or in our warehouses or offices throughout the world, we're reinventing the way people shop for their homes. Through our commitment to industry-leading technology and creative problem-solving, we are confident that Wayfair will be home to the most rewarding work of your career.

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We are looking for a Senior Backend Engineer to help us define and build the next generation of model inference within the Machine Learning Platform at Spotify. Our team's mission is to enable Spotifiers to scale their model inference – real-time, batch, or on-device. We are calculating predictions at the scale of 430M monthly active users. You will advance high-volume, real-time inference and large-scale prediction logging for future training. ML allows us to solve problems at scale, growing our impact faster than we grow our resources.

For photovoltaic materials, properties such as band gap E g are critical indicators of the material's suitability to perform a desired function. Calculating E g is often performed using Density Function Theory ( DFT) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP, CRYSTAL, CASTEP or Quantum Expresso . Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as E g of a wide range of materials.