Job description

    Buenos Aires Province
    Posting date:
    04 Jan 2022
    Job Type:
    Up to US$80000 per annum + Negotiable

Title: Machine Learning Engineer

Location: 100% Remote

Salary: $70,000 - $80,000

Are you ready for an Exciting New Challenge within a Company that specializes in Deep Learning and Visual Domain Machine Learning? If so I am hiring on behalf of my client who is looking for talented Machine Learning Scientists and Engineers to join a global team of bright and passionate engineers as they transform hCaptcha Security and Machine Learning online.

You will join a seasoned team of recognized experts, whose previous work history includes the likes of Apple, Google, Amazon and Cloudera and who have studied at institutions like Stanford and MIT, as you work on research and implementation of products and services used at scale by millions of daily users and some of the largest companies in the world.

As a Machine Learning Engineer, you will be responsible for taking cutting-edge research work in areas such as self-supervised visual representation learning, active learning, image retrieval, or generative models, and developing production-ready solutions that scale and solve business problems. You will work with real-time systems, distributed learning, and inference systems and manipulate huge datasets.


  • Together with your team of Scientists, Researchers, and Engineers, you will develop and improve abstract mathematical ideas and research-level code to yield robust production-ready implementations.
  • Keep up to date with the fast-paced ML industry and extract valuable insights from academic ML research, conference papers so you can take these ideas and implement them into Models.
  • Plan, organize, and architect robust, scalable, reliable and highly performant code solutions using modern microservice architectures, frameworks and services
  • Write clearly structured, maintainable, well documented, and tested that is high quality enough to be open-sourced.
  • Design and implement core components of ML data pipelines - dataset acquisition, feature extraction (ETL), job scheduling, data storage, augmentation, annotation, and retrieval with huge image/video datasets.
  • Help support software tooling / best practices for ML researchers to accelerate experiments; Implement efficient solutions for cloud deployment of e.g. distributed training, hyperparameter optimization, and model inference using containerized solutions.
  • Be comfortable interacting directly with large enterprise customers and startups alike when necessary, in conjunction with product, customer success, and sales teams.


  • Masters or PhD in Applied Mathematics, Physics or related numerical/scientific field.
  • 4+ years of hands-on experience in a similar role at a company using cutting edge ML tools and Deep Learning research, Computer Vision or NLP.
  • Sound mathematical knowledge (linear algebra, probability theory, stats, matrix calculus);
  • Reasonable understanding of theoretical ML principles e.g. optimization, representation learning, generalization; topics such as semi-supervised or adversarial learning, image classification, object detection, segmentation.
  • Familiarity with Python and at ideally one more programming language; Linux/shell scripting
  • Experience with Deep Learning / Scientific tools e.g. PyTorch, TF/Keras/JAX, SciKit Learn, Numpy, Pandas, OpenCV etc.
  • Creative thinker, problem solver and desired willingness for continual learning
  • Excellent communication, listening and presentation skills to and with diverse audiences and experience supporting and mentoring peers
  • Preferred: Experience with orchestration platforms: Kubernetes, containerization, and microservice design. Familiarity with distributed systems and architectures, test-driven development, CI/CD.
  • Preferred: previously published research papers in deep learning or other related fields.

Kontakt: Luchele Mendes +41 41 562 50 38

Please let the company know that you found this position on this Job Board as a way to support us, so we can keep posting cool jobs.