- UpvoteDownvoteShare Job
- Suggest Revision
10 + years of professional experience as a data scientist or machine learning engineer with proven track record of delivering functional product oriented ML solutions. Extensive experience with Python Machine Learning toolset (Tensorflow, PyTorch, Scikit-learn, Numpy, Pandas.
$250ExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
- Suggest Revision
AI/ML related capabilities such as Aure ML, AWS Sage Maker, Azure OpenAI, AWS Bedrock, etc. 4+ years' experience working with cloud providers such as AWS and Azure, esp. Working knowledge of machine learning techniques and predictive modeling.
Full-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
- Suggest Revision
Machine Learning Engineer. Good fundamentals of machine learning and deep learning. Experience in deploying models to cloud services like AWS, Azure, GCP.
$250ExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
- Suggest Revision
The Opportunity:As a Lead Machine Learning Engineer, you relish the challenge of solving business problems using the latest techniques and tools to bring models to life that deliver business value.
RemoteExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
- Suggest Revision
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. Senior Machine Learning Engineer.
$161,900 a yearExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
- Suggest Revision
Youll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications.
$165,100 a yearExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
- Suggest Revision
That's why we need you, a Machine Learning (ML) Cloud and DevOps engineer, to help us shorten the time it takes to get critical tools developed and into the hands of our Agile developers creating AI products.
$75,600 - $172,000 a yearFull-timeExpandApply NowActive JobUpdated 12 days ago - UpvoteDownvoteShare Job
- Suggest Revision
Machine Learning Software Engineer. 2+ years of experience deploying Kubernetes in a platform or cloud offering, including GCP, AWS, or Azure. Knowledge of cloud and virtualization-based technologies, including Docker, Azure, or Amazon Web Services (AWS.
$150ExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
- Suggest Revision
Job Number: R0196530 Machine Learning Software Engineer. 2+ years of experience with deploying Kubernetes in a platform or cloud offering, including GCP, AWS, or Azure. Efficient sof tware development and machine learning teams make the most of their time by limiting the activities that take developers away from writing their code.
$75,600 - $172,000 a yearPart-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
- Suggest Revision
Azure Machine Learning Architect/Lead Engineer. In-depth knowledge of Azure Machine Learning for model deployment, management, and operationalization. The AI engineer will be responsible for designing, developing, and deploying AI models based on training data sets or using generative AI. The role will focus on leveraging Azure Cloud services to build enterprise-level solutions that meet the specific needs of the organization.
Full-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
- Suggest Revision
Extensive experience in at least one cloud platform (e.g. AWS, GCP, Azure) and associated machine learning services, e.g. Amazon SageMaker, Azure ML, Databricks. Working alongside our generalist consultants, Bain's Advanced Analytics Group (AAG) helps clients across industries solve their biggest problems using our expertise in data science, customer insights, statistics, machine learning, data management, supply chain analytics and data engineering.
$260,500 - $313,000 a yearFull-timeExpandApply NowActive JobUpdated 6+ months ago - UpvoteDownvoteShare Job
- Suggest Revision
Leverage various cloud platforms such as AWS, Azure, or Google cloud to build, train, and deploy machine learning models at scale. 8+ years of experience in computer science, data science, machine learning.
Full-timeExpandApply NowActive JobUpdated 1 month ago
FEATURED BLOG POSTS
How do Employers Verify Education?
At any stage in your professional journey, you may come across an employer or a recruiter who asks to verify your educational credentials. This shouldn’t come as a surprise as 30% of candidates admitted to lying on their resumes, yet 79% of them never get caught. In fact, 85% of employers in the US who conduct background checks find that candidates have lied on their resumes or job applications.
Virtual Reality Job Interviews
With the advent of desktop computers, the arduous task of scouring through weekly job classifieds became a thing of the past. The mid-1990s brought about a new era where job seekers could easily search and apply for jobs online. The introduction of AOL's Instant Messaging feature provided an even faster means for employers and candidates to communicate and schedule interviews. As smartphones became more pervasive in the early 2000s, hiring managers increasingly used phone calls for screening and interviewing candidates. Despite this trend, over 80% of interviews still took place in person.
A Potential TikTok Ban?!
As you may already know, there has been a lot of talk lately about the possibility of a TikTok ban. While this has not yet come to fruition, it's important to consider the implications this could have for businesses and recruiters who rely on TikTok as a platform to market their brand, recruit new talent, and connect with their audience.
The Effects of Workplace Racism and Sexism
One day it's a covert statement to a mother returning to work after maternity leave. Another day it's a lingering gaze at an employee enjoying a culturally rich meal. These microaggressions (or sometimes macroaggressions) can take an employee from a confident, high-performer to one that feels insecure being themselves at work. Your employees engage with people with different ideas and feel most comfortable and valued when they can work without losing their cultural, racial, and gender identity. While most employers know this, why have workplace racism and sexism often been neglected?
When Rage Applying Strikes: How to Identify Unserious Candidates
As the job market remains highly competitive, we have seen a surge in "rage applying." This is when candidates apply to multiple jobs, often without considering whether they are truly interested in the role. Rage applying goes hand-in-hand with quiet quitting. Often, employees want to entertain the thoughts and feelings of leaving their job, but they aren't necessarily serious about leaving yet. Meanwhile, other employees engaging in this trend are actually trying to find a better role. As a recruiter, it can be hard to identify who are the real applicants in a sea full of quiet quitters, but understanding rage applying and identifying red flags will certainly help.
How to Increase Job Ad Exposure
In today's competitive job market, writing quality job ads is critical for attracting top talent to your organization. While networking and candidate referrals are prime real estate for finding qualified candidates, nothing beats the tried-and-true method of writing an extraordinary job ad. But while writing a great job ad is the first step, what's more important is increasing visibility. You could have the most detailed, well-written ad on the internet, but if no one sees it, then you are wasting time (and potentially money!). Employers often believe that job boards are the root of the problem, but you can learn how to increase job ad exposure by tweaking a few steps of your recruitment process.