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Programs on 5G, quantum computing, artificial intelligence, deep learning, quantum computing and from artificial intelligence and deep learning to cyber. programs on 5G, quantum computing, artificial intelligence, deep learning.
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We were founded in January 2016 by former executives and engineering leaders from Google and Uber. We are funded by some of the world's best investors and entrepreneurs including Founder's Fund, Khosla Ventures, First Round Capital, Eric Schmidt (Google/Alphabet Chairman), Marc Benioff (Salesforce Founder), Joe Lonsdale (Palantir Founder), Joshua Kushner (Oscar co-Founder) and Garrett Camp (Uber co-Founder).
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As a machine learning engineer, you’ll have the opportunity to immediately drive a huge, direct impact for Propel! Lead Propel in building machine learning infrastructure that all teams can take advantage of.
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Email DBA Web Sr Developer (Machine Learning, Distributed Ledger Technology, NLP, Image Analytics, Python, R, Hadoop, AWS) in McLean, VA AWS, Distributed Ledger Technology, Hadoop, Image Analytics, Java, Machine Learning, Natural Language Processing, Python, R Location: Virginia Job Function: Developer Date Of Job Posting: 07-11-2018.
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4+ years of experience implementing and applying algorithms for machine learning and artificial intelligence. Proficiency with standard machine learning tools and frameworks: Python, PyTorch, Huggingface Transformers.
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As a Machine Learning Engineer, you’ll develop ML-based metrics for evaluating an autonomous vehicle system offline at large scale. Machine Learning development experience.
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Have expertise in Machine Learning best practices (e.g. model evaluation and hyperparameter tuning, A/B test, feature engineering, feature/model selection), algorithms (e.g. gradient boosted trees, neural networks/deep learning, optimization) and domains (e.g. natural language processing, computer vision, personalization and recommendation, anomaly detection.
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As a Senior Machine Learning Engineer, You Will Be Building a Platform That Accelerates Drug Discovery With Machine Learning For a Wide Variety Of Use Cases With Respect To Data Modality, Model Architecture, And Model Size. By Leveraging Your Experience In Machine Learning, Software Engineering, Infrastructure, And Data, You Will.
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As a Google Vertex Full Stack Software Engineer, you will be responsible for developing and implementing machine learning models using Google Vertex AI and developing consumable API microservices using Typescript, Node.js, Express.
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Develop and implement train machine learning models using Google Vertex AI and Typescript, Express. Design, build, deploy machine learning models and microservices on the Google Cloud Platform.
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We are looking for a Senior Machine Learning Engineer to develop neural network architectures and algorithms to design new drug molecules and simulate their binding to human proteins, guided by our proprietary affinity calculations.
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Optimize and fine-tune machine learning models for performance and efficiency. 1 + years of experience in machine learning engineering or a related field. Stay updated on the latest advancements in machine learning and AI technologies.
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We are seeking a Full-Stack Software Engineer (Google Vertex) to join our team and transform ford.com into a customer engagement platform with modern designs and best-in-class experience. Familiarity with Front-end web development technologies (React, Vue.js, Angular, etc.
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We’re looking for a Senior Machine Learning Engineer. Senior Machine Learning Engineer. Strong understanding in the machine learning and data science technical ecosystems (e.g., Tensorflow, Pytorch, MLflow, Ray, LangChain, Data lake house (Databricks), Snowflake, SageMaker, Scikit-learn, etc.
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2 + years of experience with developing microservices and implementing CI/CD pipelines via cloud computing services like (Google Cloud Platform preferred), AWS, Azure or similar. js, Koa or other Back-end frameworks.
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machine learning jobs Title: devops engineer Company: Tusimple Inc
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A Potential TikTok Ban?!
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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.