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Software company in midtown is actively looking for Lead Core Java Backend Engineer to help build out their high volume platform. Candidate must be interested to work side by side with Data Scientists implementing Machine Learning models and doing design and implementation of their existing production system.
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Strong programming skills in languages such as Python or Java with experience in libraries/frameworks like TensorFlow, PyTorch, scikit-learn, etc. Model Development: Lead the development of machine learning models, including data preprocessing, feature engineering, model training, and evaluation.
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Utilize programming languages like Java, Python, SQL, Node, Go, and Scala, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services.
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The Machine Learning Experience (MLX) team supports an internally hosted Model Training ecosystem running critical infrastructure for machine learning and data analysis across Capital One. You will get an opportunity to collaborate on the 1000's of models being trained on the platform by 4000+ users and dive deep into the unique engineering challenges we have around compute, data access, security and scale.
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Design, develop, and maintain software applications using Java, Spring Boot, TypeScript, HTML, CSS, MongoDB, and SQL. Or, join our core engineering teams, and elevate all of our businesses by providing reliable, scalable platforms for data engineering, machine learning, networking, developer tooling, collaboration and more.
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Train and fine-tune machine learning models to optimize their performance. Solid understanding of machine learning algorithms and techniques. Proficiency in programming languages such as Python, Java, or C.
$140,000 - $190,000 a yearFull-timeRemoteExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Implement Machine Learning (ML) and Big Data platforms in Hybrid and multi-cloud environment specifically in AWS SageMaker environment. Support the building of machine learning, data platforms, and infrastructure required for optimal data extraction, transformations, and loading of data from a wide variety of data sources.
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Primary responsibility will be to work closely with the Data Engineering team, Data Architects and Machine Learning Team to implement data solutions for the organization using Python, Java, Kafka and other big data solutions, creating technical specification documents and test plans.
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Strong proficiency in programming languages such as Python, Java, or Scala, with expertise in data processing frameworks and libraries (e.g., Spark, Hadoop, SQL, etc.) A Glimpse into the Daily Routine of a Machine Learning Engineer.
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It is a bonus if you have experience in Java, Airlow, Kubeflow, MLFlow, BigQuery. You have a Bachelor’s Degree in Computer Science, Machine Learning, Data Science, or equivalent experience.
Full-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics.
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You have practical experience in one or more of the following programming languages: Scala, Python, Java, or R. · Leverage your broad familiarity with basic concepts in probability and statistics, along with exposure to basic foundations of computer science, graph mining, and machine learning.
$123,900 - $227,200 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience with NLP/Machine learning. Experience with Java, Scala, Microservices. Big Data Engineer is an intermediate level position responsible for participation in the establishment and implementation of new or revised application systems and programs in coordination with the Technology team.
$121,200 - $181,800 a yearFull-timeExpandApply NowActive JobUpdated 4 days ago - UpvoteDownvoteShare Job
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Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action.
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Experience in mathematics (statistics, linear algebra, differential calculus); data visualization (Tableau, Power BI, Qlikview); programming (SQL, Python, R, Java); data analysis (feature engineering, data wrangling, EDA) and machine learning (classification, regression, reinforcement learning, deep learning, clustering, dimensionality reduction.
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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.
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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?
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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.
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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.
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Building a Candidate Pipeline Through Internships
Building a candidate pipeline through a great internship program for local college students and recent graduates at local universities is a great and cost-effective way to attract and retain top talent. By offering meaningful and impactful work experiences, regular feedback, coaching, and mentorship, you can create a positive internship experience that will make your organization a sought-after destination for future employees. This not only benefits the organization in the short-term but also in the long-term, as you'll have a pool of well-trained and experienced candidates who may be interested in full-time employment once they graduate. Furthermore, building relationships with local universities and college students can increase brand awareness and build a positive reputation for your organization in the local community.