Jobs Companies Caylent Senior Machine Learning Engineer - Not an Active Opening, Building Talent Pipeline

Sobre esta vaga de Senior Machine Learning Engineer - Not an Active Opening, Building Talent Pipeline na Caylent

Caylent · ARGENTINA

Caylent is a cloud native services company that helps organizations bring the best out of their people and technology using Amazon Web Services (AWS). We provide a full-range of AWS services including workload migrations and modernization, cloud native application development, DevOps, data engineering, security and compliance, and everything in between.

At Caylent, our people always come first.  We are a global company and operate fully remote with employees in Canada, the United States, and Latin America. We celebrate the culture of each of our team members and foster a community of technological curiosity. Come talk to us to learn more about what it means to be a Caylien!

Note: This isn’t an active role right now, but we’re building a community of great talent for future opportunities at Caylent. If your background aligns with what we’re looking for, our team may reach out to learn more about you and explore potential future fits.

The Mission

At Caylent, a Senior Machine Learning Engineer works as an integral part of a cross-functional delivery team to design and document machine learning solutions on the AWS cloud for our customers.  We are looking for someone that has a strong understanding of the various model types and tools, and can help our customers connect their business goals with the details of feature design, model training and inference.  You will develop solutions designed by an architect.

You will participate in daily standup meetings with your team and bi-weekly agile ceremonies with the customer.  Your manager will have a weekly 1:1 with you to help guide you in your career and make the most of your time at Caylent.

Your Assignments

  • Work with a team to deliver machine learning solutions on AWS for customers
  • Participate in and contribute to daily standup meetings
  • Develop and implement ML models, MLOps, and analytics
  • Big data processing and preparation of training data for models

Your Qualifications

  • Strong experience in building ML models for real world applications
  • Strong experience in at least one of these:
    • AWS ML Services/SageMaker
    • ML libraries like Keras, Tensorflow, PyTorch, Scikit-learn
    • MLOps tools such as MLflow, Kubeflow, Airflow
    • Advanced analytics using time series forecasting and/or inferential statistics
  • Strong experience in one or more of these data processing solutions:
    • Big data processing platforms like Spark, Hadoop, or streaming platforms
    • Data processing and cleansing using Python/Pandas, PySpark, Scala, SQL
  • Strong understanding of feature definition, model meta-data, hyperparameter tuning, stochastic gradient descent, deep learning layer types and activation functions
  • Experience in visualization using SageMaker, ggplot, matplotlib, or seaborn
  • Experience with an IaC tool such as CloudFormation, Amazon CDK or Terraform
  • Excellent written and verbal communication skills

Benefits 

  • Pay in USD
  • 100% remote work
  • Generous holidays and flexible PTO
  • Competitive phantom equity
  • Paid for exams and certifications
  • Peer bonus awards
  • State of the art laptop and tools
  • Equipment & Office Stipend
  • Individual professional development plan
  • Annual stipend for Learning and Development
  • Work with an amazing worldwide team and in an incredible corporate culture

This role may require up to 25% travel, depending on business needs. 

NOTE: We’re unable to provide visa sponsorship now or at any time in the future.

At Caylent, we are committed to fair, transparent, and inclusive hiring practices. As part of our recruitment process, we may use artificial intelligence (AI) tools or automated systems to assist with the screening and evaluation of applications to help match candidate qualifications with job requirements.
These tools are designed to support — not replace — human decision-making. Final hiring decisions are always made by our trained recruitment professionals.
If an AI or automated tool is used during your application process, it will only be in accordance with applicable laws and regulations, and your information will be handled in a secure and confidential manner.
If you have any questions, please contact talent@caylent.com 

Caylent is a place where everyone belongs. We celebrate diversity and are committed to creating an inclusive environment for all employees. Our approach helps us to build a winning team that represents a variety of backgrounds, perspectives, and abilities. So, regardless of how your diversity expresses itself, you can find a home here at Caylent.  

We are proud to be an equal opportunity employer. We prohibit discrimination and harassment of any kind based on race, color, religion, national origin, sex (including pregnancy), sexual orientation, gender identity, gender expression, age, veteran status, genetic information, disability, or other applicable legally protected characteristics. If you would like to request an accommodation due to a disability, please contact us at hr@caylent.com.
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Como este salário de ML Engineer se compara

Esta vaga paga $208,000/yrem linha com da faixa típica para vagas de ML Engineer.

$140,400 a mediana $205,000 $300,000

Faixa típica $172,500–$250,000/yr, com base em 743 vagas de ML Engineer comparáveis na JobsRadar (pagamento anualizado em USD). Ver insights salariais de ML Engineer →

Sobre a Caylent

Caylent is a Cloud Native services provider serving technology-enabled companies ranging from venture-backed startups to Fortune 500 enterprises. Our employees enjoy being on the cutting edge of technology while enjoying our fully-remote company and culture.

With employees from over 16 different countries and women well-represented on our leadership team, we like to think that we’re a different kind of tech company. Made up of employees who have come from other top cloud consulting partners and cutting-edge companies, we saw that there was a better way – one that included diversity, a genuinely helpful culture and most of all – fun. We take the work we do very seriously, but never ourselves. 

 

 
 
 

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