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Working closely with our data & analytics team and clinical trial experts, this role will harness the power of cutting-edge AI and LLM (Machine Learning and Deep Learning) technologies for enhancing decision-making in the field of clinical trial process.
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As a Machine Learning Automation Engineer on the Behavioral Automated Triage team, you will interrogate data at scale, perform data engineering, leverage deep learning models (i.e. foundation models and language models), and respond to the evolving data needs at Zoox.
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We are looking for a full-time algorithm Research Scientist who is passionate on pioneering Machine Learning, Deep Learning, Reinforcement Learning, Foundation Models for image processing and computer vision applications in semiconductor process control business.
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Successful individuals will have past experience deploying real world deep learning models to production, having functioned in roles such as machine learning scientist, machine learning engineer, applied AI scientist, or similar.
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We are seeking a skilled and innovative Machine Learning Engineer to join our team. Proven experience as a Machine Learning Engineer or in a similar role. Design, build, and deploy machine learning models to improve various aspects of our platform, including customer personalization, predictive analytics, and automated decision-making.
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Model Development: Design, build, and deploy machine learning models to improve various aspects of our platform, including customer personalization, predictive analytics, and automated decision-making.
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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 toolsets (Tensorflow, PyTorch, Scikit-learn, Numpy, Pandas.
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As a Senior/Staff Machine Learning Engineer you are someone who has significant expertise in modeling, statistics and programming. A PhD degree in Machine Learning or Deep Learning is a massive plus.
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We’re looking for a Senior Machine Learning Engineer. Senior Machine Learning Engineer. Deep understanding of Machine Learning best practices (eg.
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We are seeking a highly motivated Machine Learning Engineer Co-Op from July 2024 to December 2024, focusing on Natural Language Processing (NLP) and Computer Vision applications. As a Machine Learning Engineer Co-Op, you will work closely with cross-functional teams, including Data Science, Software Engineering, and Product Management, to develop machine learning models, implement solutions, and ensure the successful deployment and maintenance of these models in production.
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You will work with a team of machine learning researchers to build AI software systems, learn about deep learning algorithms, and use your technical skills to advance autonomous driving.
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Have experience applying signal processing, image processing, pattern recognition, machine learning, deep learning, or related techniques with respect to signal extraction from sensors.
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As a Machine Learning Engineer, Openings Studio, you will roll up your sleeves to help us discover new and interesting ways to make our users’ lives faster and easier than ever before, leveraging both traditional machine learning techniques and Large Language Modeling (LLM) and Generative AI solutions.
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We are seeking a machine learning engineer to work closely with our product and research teams to develop SOTA deep learning software. Solid knowledge of machine learning and deep learning fundamentals with experience in one or more of computer vision, NLP, speech, reinforcement learning, generative models, etc.
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Job description : Machine Learning Engineer, Senior. #Machine #Learning #Engineer #Senior. Machine Learning Engineer, Senior. participate in partnerships with data science leaders, like our partnership with NVIDIA to deliver Deep Learning Institute (DLI) training to the federal government.
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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.