Сучасна диджитал-освіта для дітей — безоплатне заняття в GoITeens ×
Вже 20 років ми працюємо з ринками Ізраїлю, Європи, Америки та Канади. Наша місія — допомогати молодим компаніям втілювати свої ідеї в життя. Ми маємо понад 200 успішних проектів та 300 млн. інвестицій, отриманих стартапами. У нашій компанії працюють 150+ розробників рівня Senior та Middle, що з радістю поділяться своїм досвідом.
14 листопада 2022

MLOps/Data Engineer — $1000 Bonus for recommendation (вакансія неактивна)

віддалено

Would you like to recommend someone for this position? Send your friend’s resume to the e-mail — [email protected] — and get a bonus of $ 1000 upon completion of the specialist’s probationary period.

  • M.Sc. (Phd preferred) in Computer Science, Engineer, or equivalent, ideally with a thesis
    in deep learning
  • Proven track record in MLOps and in Data Engineering
  • High proficiency in Python and its data science stack (Pandas, sklearn, etc.).
  • Deep understanding of artificial deep neural networks architectures, algorithms,
    infrastructure, tooling and practices, ideally in NLP/NLU/NLG.
  • Hands-on experience with design, implementation and optimization of deep learning
    models using common frameworks (TensorFlow, PyTorch, HuggingFace, etc.)
  • Model optimization techniques — familiarity with testing and hyperparameter optimization tools and frameworks.
  • 5+ years of hands-on experience in engineering in production environments
  • 3+ years of experience in ML
  • Experience in the following technologies: Dockers, Kubernetes, AWS, MongoDB
  • End to end experience — owning feature from an idea stage, through design architecture, coding, integration, deployment, and monitoring stages

Nice to have requirements

  • NLP/NLU/NLG experience in document classification, text generation, summarization,
    NER
  • Proven ability of conducting reproducible applied research in ML
  • Working with medical data on healthcare data projects
  • Experience in the following technologies: Spark/Hadoop, Airflow, Redis, Kafka
  • Go programming language proficience

We offer:

  • People-oriented management without bureaucracy
  • The friendly climate inside the company is confirmed by the frequent comeback of previous employees
  • Flexible working schedule
  • Paid time off (18 working days per year, plus all national holidays and 9 sick days)
  • Full financial and legal support for private entrepreneurs
  • Education compensation
  • Free English classes with native speakers or with Ukrainian teachers (for your choice)
  • Dedicated HR

Responsibilities:

— Building and improving our engineering infrastructure to enable and scale our applied
algorithmic research and development. On a day to day, some of your responsibilities will
include the:
— Design, Develop, Test, and Maintain ETLs and services for integrating with our
customer’s various data systems.
— Design, Develop, Test, and Maintain ETLs and services for building our own data
processes to accommodate applied research needs as well as production needs.
— Finding performance bottlenecks in our data pipelines and our machine learning
pipelines and resolving them
— Develop end-to-end algorithmic solutions for complex ML problems — from research and
training models, through design, development, evaluation, and optimization.
— Develop train and inference engine pipelines in a large-scale distributed system.
— Transform NLP and data-related ML/DL algorithmic approaches into efficient and
optimized production-ready solutions.
— Design, Implement and Optimize ML/DL and research pipelines to improve the algorithm’s
performance.
— Transform high-level product requirements into technical requirements
— Brainstorm and prototype algorithmic improvements.
— Work in an ambiguous environment and collect requirements from different personas in
the company (Product, FE, Research, etc.)
— Advise and collaborate with researchers on DL software engineering aspects (such as
tools and practices)

Our client implements Artificial Intelligence innovations in medical coding. They believe that medical coding should become fully automated and they know how to help this to happen.

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