Data Engineer (Deep Learning)

We are looking for a versatile data engineer who can effectively contribute into deep learning model development. You are experienced with building modern data-driven solutions and are comfortable diving deep into real-life data. You will be working as part of our R&D team drawing experience from e.g. developing Amazon Alexa and Apple Siri.

You will be a key technical expert in our experienced R&D team responsible for:

  • Processing and curating large amounts of machine learning training data
  • Analyzing, optimizing, and evaluating the performance critical model components
  • Developing and maintaining scalable machine learning training infrastructure
  • Implementing highly efficient state-of-the-art deep learning algorithms

You will be a great candidate for this position if you have:

  • Experience on large-scale data processing
  • Hands-on experience on state-of-the-art deep learning model training pipelines
  • Understanding of machine learning frameworks (PyTorch/TensorFlow)
  • Been involved in modern software development projects
  • Entrepreneurial mindset
  • Good team work skills

Tools and technologies we are using:

  • Programming languages: Python, C/C++, Unix scripting
  • Big data processing: Streaming eg. Kafka, Batch e.g. Spark
  • Machine learning: PyTorch, TensorFlow
  • Cloud: AWS, Google Cloud
  • DevOps: Docker, Kubernetes
  • GPU programming: CUDA

We can offer you a once in a lifetime opportunity to build something amazing with us, and of course also:

  • Opportunity to work with state-of-the-art technologies
  • Competitive salary
  • Employee equity plan
  • Liberal remote work policy (however not fully remote)
  • Attractive perks and benefits
  • Awesome office space
  • Top-notch kit (latest Macs etc)
  • Opportunity to work with a world class & fun team
  • Opportunity to contribute into creating our culture

To apply

Please submit your resume and cover letter to careers@speechly.com.Please describe in your cover letter how you have been developing data pipelines for modern big data systems and what have you learned while doing that.