Senior Machine Learning Scientist at Metropolis United States › Washington › Seattle (Posted Mar 9 2023) Please mention that you found the job at Jobhunt.ai Apply now! Do they allow remote work? Remote work is possible, see the description below for more information. Job description Seattle, WA or Remote The Company Metropolis develops advanced computer vision and machine learning technology that make mobile commerce remarkable. Our platform is already deployed in hundreds of mobility facilities and industries with billions of dollars in opportunity.

While education, skills, and experience provide the technical foundation and are essential for a capable machine learning (ML) team, the team will only turn into a strong and successful one when combined with the right soft skills. Education, skills, and experience are very important traits for a capable ML expert. Having a solid educational background in mathematics, computer science, the natural sciences, and statistics provides a strong foundation for understanding the underlying theories and algorithms that drive ML models. In addition, acquiring practical skills through hands-on experience with various programming languages, libraries, and tools is critical for implementing and deploying successful ML solutions. However, the right set of soft skills can transform a team of capable ML experts and turn them into a successful one. These skills complement technical skills.

Aspirion is an industry-leading provider of complex claims management services. We specialize in Motor Vehicle Accidents, Worker's Compensation, Veterans Administration and Tricare, Complex Denials, Out-of-State Medicaid, and Eligibility and Enrollment Services. Our employees work in an environment that is both challenging and rewarding. Aspirion helps hospitals and other healthcare providers get claims paid correctly by insurance companies, enabling providers to dedicate more resources to patient care and lowering the financial burden on patients. As a tech-enabled services business, Aspirion is investing heavily to embed user-centric design, automation, and machine learning into Compass, our proprietary internal workflow platform supporting the operations of multiple service lines.

In this article, we shed a light on how to identify issues in making Machine Learning (ML) activities more efficient primarily through model management capabilities. However, we do not propose any concrete solution but provides a framework to systematically think about the model management problem. Such a framework is useful to consider for teams that are on the verge of industrializing or scaling their ML systems to address technical debt or gaps while handling ML models. If this is interesting for you, please keep reading.

A trend we've been tracking for several years now is how the data science profession has steered away from being entirely independent, do-it-all unicorns into a more specialized work. It's by no means that individuals with deep knowledge in several domains have disappeared, but rather, the need for data science has grown, and teams have increased in headcount. It's not just that there are more cooks in the kitchen, but also machine learning solutions are much more ambitious in scope. It's becoming more important to think about the competencies of a team rather than expecting every individual to be an expert at everything related to machine learning. This is very similar to software engineering roles diverging into backend, front-end, and DevOps engineers. Each of them focuses on a different part of the machine learning system.

The Machine Learning team at Mozilla continues work on DeepSpeech, an automatic speech recognition (ASR) engine which aims to make speech recognition technology and trained models openly available to developers. DeepSpeech is a deep learning-based ASR engine with a simple API. We also provide pre-trained English models. Our latest release, version v0.6, offers the highest quality, most feature-packed model so far. In this overview, we'll show how DeepSpeech can transform your applications by enabling client-side, low-latency, and privacy-preserving speech recognition capabilities.

By Lukas Biewald, Founder, Weights and Biases; Founder, Figure Eight (formerly known as CrowdFlower). Lately, friends at companies of all sizes and industries have been asking me the same question, "How do I apply machine learning to my business?" These folks know enough to have a sense of good use cases for machine learning. Where everyone gets stuck is actually making it work, hiring people, and making them successful. I'll outline my three main approaches depending on the size of your business.

Everyone from chief executive officers to product managers to venture capitalists wants to understand machine learning better. They know it has the potential to take their software to the next level. They feel the excitement around it. They've read the TechCrunch or Fortune articles, and they've maybe even done a quick linear regression or two. But the primary issue that many product leaders grapple with when it comes to machine learning is that they want programs that don't just crunch the numbers, but can also think for them.

ServiceNow is changing the way people work. With a service-orientation toward the activities, tasks and processes that make up day-to-day work life, we help the modern enterprise operate faster and be more scalable than ever before. If you thought you knew about ServiceNow and what we do, take a look again! Our products lines are diverse and robust. The Enterprise Cloud is dynamic, scalable to billions of transactions weekly, and global in scope and size.

My name is Alessia Nigretti and I am a Technical Evangelist for Unity. My job is to introduce Unity's new features to developers. My fellow evangelist Ciro Continisio and I developed the first demo game that uses the new Unity Machine Learning Agents system and showed it at DevGamm Minsk 2017. This post is based on our talk and explains what we learned making the demo. At the same time, we invite you to join the ML-Agents Challenge and show off your creative use-cases of the toolkit.