Kubernetes Jobs in Web3

1,713 jobs found

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Job Position Company Posted Location Salary Tags

Trustana

New York, NY, United States

$67k - $75k

Crypto.com

Taipei, Taiwan

$157k - $175k

Swissblock

Zug, Switzerland

$90k - $106k

Swissblock

Zug, Switzerland

$105k - $120k

Kiln

Paris, France

$90k - $100k

StackedSP Inc

Remote

$120k - $170k

Bitcoin.com

Dallas, TX, United States

$122k - $123k

Consensys

Remote

$118k - $209k

Consensys

Remote

$90k - $105k

WOO

Taipei, Taiwan

$98k - $111k

Keyfactor

Remote

$98k - $156k

Magic

New York, NY, United States

$90k - $145k

Trust Machines

New York, NY, United States

$180k - $220k

Ramp

Warsaw, Poland

$85k - $148k

BitGo

Palo Alto, CA, United States

$150k - $200k

Nethermind

Istanbul, Turkey

$90k - $100k

Matter Labs

Remote

$67k - $150k

WOO

Taipei, Taiwan

$71k - $115k

0x

San Francisco, CA, United States

$36k - $54k

0x

San Francisco, CA, United States

$95k - $230k

Ripple

London, United Kingdom

$157k - $175k

Eigen Labs

Seattle, WA, United States

$225k - $250k

Numus

Remote

$91k - $97k

FalconX

Remote

$106k - $165k

BitGo

New York, NY, United States

$170k - $220k

Trustana
$67k - $75k est.
BE Berlin, Berlin, Germany
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Machine Learning Engineer

Job Description:

You will drive innovation through data engineering, machine learning and efficient deployment strategies. The ideal candidate will posses a robust comprehension of ML principles and their scientific underpinnings, while seamlessly applying this knowledge within an engineering and product focused environment.

Responsibilities:

Data Engineering: Design and develop robust data pipelines for acquiring, preprocessing, and transforming diverse datasets to support machine learning models. Implement scalable solutions for data ingestion, storage, and retrieval.

Machine Learning Development: Utilize state-of-the-art machine learning techniques to build predictive, generative models and recommendation systems. Focus on Natural Language Processing (NLP), including large language models (LLM). It's nice to have multi-modal capabilities and proficiency in Computer Vision techniques.

Model Deployment & Evaluation: Implement efficient and scalable deployment pipelines for machine learning models, ensuring seamless integration into production environments. Collaborate with DevOps and software engineering teams to automate deployment processes and monitor and evaluate model performance in real time.

Continuous Improvement: Stay updated with the latest advancements in the ML space. Proactively identify opportunities to enhance existing models and pipelines, driving innovation and efficiency.

Requirements:

  • Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or related field.
  • Strong understanding of software engineering best practices, version control systems, CI/CD, and agile development methodologies.
    Proven experience in data engineering, including data acquisition, preprocessing, and ETL.
  • Proficiency in programming languages such as Python, with experience in ML frameworks like PyTorch, TensorFlow and libraries like HuggingFace, Pandas, Bokeh.
  • Experience designing, training, and deploying machine learning models in production environments encompassing containerization technologies like Docker, cloud platforms, and model-serving frameworks like TorchServe and MLFlow. Scaling strategy experience in a high-throughput, low-latency scenario is desirable. Additionally, familiarity with advanced DevOps capabilities, such as Kubernetes, is nice to have.
  • Good communication skills and ability to collaborate effectively in a team environment.
  • Previous exposure to web or e-commerce applications and an understanding relevant industry challenges and requirements is desirable.

Note: This job description is not exhaustive. We encourage candidates to apply even if not all conditions are met, as we will provide professional growth opportunities.

Is Kubernetes high demand?

Yes, Kubernetes is currently in high demand in the technology industry

Kubernetes is an open-source container orchestration platform that is widely used for deploying, scaling, and managing containerized applications

It provides a standardized way to manage and automate the deployment of containerized applications across multiple hosts and provides benefits such as reliability, scalability, and flexibility

As more and more organizations move towards containerized architectures, Kubernetes has become a critical component of their infrastructure

Kubernetes is used by companies of all sizes, from startups to large enterprises, and across various industries, including finance, healthcare, and e-commerce

According to various job market and salary surveys, Kubernetes-related skills are in high demand, and job positions related to Kubernetes are growing at a rapid pace

In fact, Kubernetes is often listed as one of the top skills that are in high demand by technology companies

Overall, Kubernetes is a highly sought-after skill in the technology industry, and it's likely to remain in high demand in the foreseeable future as more and more organizations adopt containerization and cloud-native architectures.