AI-mployed的动态

DVA is not associated with this job posting Machine Learning Engineer (MLE) https://lnkd.in/gpST_5Ku A Machine Learning Engineer trains, validates and evaluates machine learning and deep learning models which perform predictive tasks in various domains such as computer vision, natural language understanding and time-series analysis. Some examples include visual recognition models, activity detection using sensor data and demand forecasting. Translate and refine business goals into appropriate machine learning objectives. Design and implement ML/DL solutions and integrate them with various Big Data platforms and architectures. Create and maintain ML pipelines that are scalable, robust, and ready for production. Collaborate with domain experts, software developers, and data scientists. Troubleshoot ML/DL model issues, including recommendations for retrain, re-validate, and improvements/optimization. #remotework #workfromhome #virtualoffice #telecommute #digitalnomad #remoteteam #flexiblework #remoteworklife #remoteworking #remoteworkforce #remoteworkculture #remoteworktips #remoteworksuccess #remoteworkperks #remoteworkbenefits #remoteworkstrategies #remoteworkproductivity #remoteworkbalance #remoteworkcommunity

#hiring Senior Machine Learning Engineer, Houston, United States, fulltime #jobs #jobseekers #careers #Houstonjobs #Texasjobs #ITCommunications Apply: https://lnkd.in/dwkbFGDK External Description:LIVING OUR VALUESAll associates at The Friedkin Group are guided by Our Values, which are the unifying foundation of our companies. We strive to ensure that every decision we make and every action we take demonstrates Our Values. We believe that putting Our Values into practice creates lasting benefits for all our associates, shareholders, and the communities in which we live.JOB SUMMARYAs a Senior Machine Learning Engineer, you will work on building AI/ML solutions across a wide range of business applications within The Friedkin Group of companies. You will drive the development and deployment of state-of-the-art AI services and analytic applications that support the needs of our business. We are looking for a driven and talented individual who has a strong background in software engineering and a deep understanding of AI/ML frameworks. You will work closely with data scientists, machine learning engineers, and other software engineers, using the latest tools and technologies to develop analytic solutions and integrate analytic models with existing business applications.ESSENTIAL FUNCTIONSWork closely with product managers to understand business requirements and translate them into technical solutions.Collaborate with data scientists, data engineers, data analysts, software engineers, IT specialists, and stakeholders to expand effective use of AI applications.Collaborate with cross-functional teams to design, develop, and maintain highly complex AI/ML systems.Develop and implement AI/ML interfaces, services, and analytic applications to support the company's initiatives and projects.Deploy machine learning models into production environments, ensuring scalability, reliability, and real-time performance. This may involve containerization, API development, and integration with existing systems.Optimize machine learning algorithms and infrastructure for performance, scalability, and cost-efficiency. This may involve parallelization, distributed computing, and resource management.Develop User Interfaces

Looking to become a Machine Learning Engineer in 2024? Great choice — it's a varied and exciting role with lots of opportunities for growth. A typical day for a machine learning engineer involves coding, experimenting with different algorithms, debugging, and optimizing models. Machine learning is also not limited to a single industry; it is used in healthcare, finance, e-commerce, and more. This diversity allows machine learning engineers to explore different areas and apply their skills to real-world challenges. Check out this article for more details: https://heyor.ca/QUV8H7 #MachineLearning #AI #TechCareers #DataScience

Difference between various machine learning (ML) roles: - Data Engineer:?Responsible for building data collection pipelines and managing data flow for all ML applications. - Data Scientist:?Analyzes data to extract business insights, using ML algorithms for prediction and segmentation. - Applied Scientist:?Applies scientific knowledge and research methods to solve real-world problems, focusing on applying existing ML models to specific industries. - ML Engineer:?Implements ML models into software products, requiring strong software engineering skills. - Research Scientist:?Explores new ML domains, develops novel models, and publishes research findings. - Research Engineer:?Collaborates with Research Scientists to implement and test new ML ideas. #AI #MachineLearning

?? Exciting times ahead in the AI sector! This comprehensive guide highlights some of the top-paying roles. Whether you're just starting or looking to pivot into a high-demand field, these positions offer great potential for growth and earnings. Perfect for those curious about their future in technology! ???? #AICareers #TechJobs #AI

Looking to become a Machine Learning Engineer in 2024? Great choice — it's a varied and exciting role with lots of opportunities for growth. A typical day for a machine learning engineer involves coding, experimenting with different algorithms, debugging, and optimizing models. Machine learning is also not limited to a single industry; it is used in healthcare, finance, e-commerce, and more. This diversity allows machine learning engineers to explore different areas and apply their skills to real-world challenges. Check out this article for more details: https://heyor.ca/QUV8H7 #MachineLearning #AI #TechCareers #DataScience

DVA is not associated with this job posting Machine Learning Engineer https://lnkd.in/gajPh9xa ROLE OVERVIEW: As a Machine Learning Engineer at Nelnet, you'll be at the intersection of data science and software engineering. Your primary focus will be on designing, building, and deploying machine learning models to solve various business problems. You'll collaborate closely with Data Scientists, Data Engineers, and business stakeholders to bring machine learning solutions from conceptualization to production. You will also work extensively with large language models, implementing them in various applications to enhance our AI capabilities. JOB RESPONSIBILITIES: 1. Model Development: Create, validate, and deploy machine learning models using advanced algorithms and frameworks like TensorFlow, PyTorch, or scikit-learn. 2. Feature Engineering: Collaborate with Data Scientists to engineer features for machine learning models. 3. Model Optimization: Continuously refine and optimize models for improved performance and efficiency. 4. Scalability: Design and implement scalable machine learning pipelines using cloud-based solutions on platforms like AWS, leveraging tools such as Terraform for infrastructure management. 5. Large Language Models: Work with large language models, implementing them in various applications to provide advanced AI solutions. 6. Collaboration: Work closely with Data Engineers to ensure seamless data integration for machine learning models. 7. Monitoring: Implement monitoring tools to track the performance and outcomes of deployed models. Utilize CI/CD pipelines to automate the deployment and maintenance of machine learning solutions. 8. Documentation: Maintain thorough documentation for model architectures, data flows, and performance metrics. 9. Innovation: Stay updated on industry trends and emerging technologies to incorporate into existing systems. 10. Mentorship: Provide guidance and mentorship to junior team members in machine learning best practices. #recruiting #nowhiring #hiring #jobs #jobsearch #job #recruitment #careers #recruiting #hiringnow #employment #career #jobseekers #jobopening #work #jobhunt #resume #jobopportunity #applynow #jobsearching #jobseeker #hr #staffing #jobshiring #cfbr #jobinterview #vacancy #recruiter #jobalert #business #joinourteam

DVA is not associated with this job posting Senior Machine Learning Engineer Global https://lnkd.in/g5aNPGwG We’re looking for a Senior Machine Learning Engineer to join our quickly growing team and make a big impact. As a Senior Machine Learning Engineer in the Tech org, you’ll work closely with data scientists, software engineers, product managers, and designers, to develop Machine Learning technologies which help simplify the jobs of our users. You’ll work on major technical projects with large data volumes, lead the building of new features, and help shape our engineering culture and processes. Our technology team is focused on scale, quality, delivery, and thoughtful customer experience. To be set up for success in this role, you’ll need to have: 5+ years total professional experience building Machine Learning products within user-facing software apps A capacity for a high degree of autonomy and out-of-the-box thinking A commitment to collaborating closely with other engineers and across functions If any of the below also describe you, this could be an exciting opportunity: Worked on a complex, high-traffic site at a startup or software-as-a-service company, ideally with large amounts of data Experience with text modeling, NLP, and large language models Experience serving GPU-intensive models in large-scale production environments Interest in search Interest in journalism, news, media or social media #business #entrepreneurship #leadership #strategy #innovation #growth #success #management #marketing #sales #finance #productivity #networking #professionaldevelopment #career #teamwork #startup #smallbusiness #digitalmarketing #branding #customerexperience #technology #economy #investing #consulting #entrepreneurmindset #leadershipdevelopment #businessowner #worklifebalance #corporateculture

Data Scientists vs. Machine Learning Engineers: Roles & Responsibilities Comparison Both data scientists and machine learning engineers play crucial roles in the world of data analysis and AI, but their specific focuses and responsibilities differ: Data Scientist: Focus: Extracting insights and knowledge from data. They understand the business problem, explore and analyze data, build statistical models, and interpret results to inform decision-making. Key Skills: Statistics, probability, data analysis techniques, programming (Python, R), data visualization, communication, business acumen. Responsibilities: Data cleaning and preparation Exploratory data analysis Feature engineering Building and analyzing statistical models Communicating findings to stakeholders Staying up-to-date on new data analysis techniques Machine Learning Engineer: Focus: Building and deploying machine learning models into production systems. They design, train, and optimize models, ensuring they're scalable, efficient, and reliable in real-world applications. Key Skills: Programming (Python, C++), machine learning algorithms, software engineering principles, cloud computing, distributed systems. Responsibilities: Implementing machine learning pipelines Selecting and training machine learning models Hyperparameter tuning and model optimization Deploying models to production Monitoring and maintaining model performance Integrating models with other systems Here's a table summarizing the key differences: Feature Data Scientist Machine Learning Engineer Focus Insights and knowledge extraction Building and deploying ML models Main Skills Statistics, data analysis, communication Programming, ML algorithms, software engineering Responsibilities Data cleaning, analysis, modeling, communication Model development, training, deployment, monitoring Similarities and Collaboration: Both roles require strong analytical and problem-solving skills. Both use programming languages like Python and R. They often collaborate on projects, with data scientists providing insights and engineers building and deploying models. Which role is right for you? Consider your interests and skills: If you enjoy exploring data, uncovering patterns, and communicating insights, data science might be a good fit. If you're passionate about building and optimizing software systems, and enjoy working with ML algorithms, machine learning engineering might be your calling. Remember, these are general outlines, and specific roles can vary depending on the company and industry. #ai #datascientists #mlengineer #datascience #ml #artificialintelligence

Machine Learning Engineer vs Data Scientist: Decoding the Differences In the rapidly evolving tech landscape, two roles often cause confusion: Machine Learning (ML) Engineer and Data Scientist. While both work with data and AI, their focus and responsibilities differ significantly. Let's break it do ● Data Scientist: The Explorer ? Focus: Analyzing data to extract insights and create predictive models ? Key Responsibilities: - Data cleaning and preprocessing - Exploratory data analysis - Statistical modeling and hypothesis testing - Data visualization - Communicating insights to stakeholders ? Core Skills: - Strong statistical knowledge - Proficiency in Python/R and SQL - Data visualization tools - Business acumen ● Machine Learning Engineer: The Builder ? Focus: Designing, building, and deploying scalable ML systems ? Key Responsibilities: - Developing and optimizing ML algorithms - Creating data pipelines - Deploying models to production - Monitoring and maintaining ML systems - Scaling ML solutions ? Core Skills: - Software engineering expertise - Proficiency in ML frameworks (TensorFlow, PyTorch) - Cloud platforms (AWS, GCP, Azure) - System design and architecture ● Collaboration and Overlap ? Data Scientists and ML Engineers often work together on projects ? A Data Scientist might develop a prototype model, which an ML Engineer then scales and deploys ? Both roles require an understanding of ML algorithms and programming skills ● Career Implications ? Data Scientists drive innovation through data-driven insights ? ML Engineers bring these innovations to life at scale ? Understanding the distinction helps professionals chart their career paths ? Companies benefit from having both roles to build effective data teams As AI continues to reshape industries, both Data Scientists and Machine Learning Engineers play crucial, complementary roles in leveraging data for business success. #MachineLearningEngineer #DataScientist #MLvsDataScience #AIcareers #TechRolesComparison #DataScience #MachineLearning #CareerInTech #MLEngineering #DataScienceInsights #ArtificialIntelligence #TechIndustry #MLvsDS #DataDrivenDecisions #AIandDataScience #DanishAmmar Note:image used in this blog is ai generated