We are looking for a Machine Learning/NLP Engineer to join our team in London.
As a Machine Learning/NLP Engineer you will be responsible for both advancing the implementation efforts of our Machine Learning and NLP pipelines for large-scale deployment and training, as well as conducting research experiments that advance our capabilities and understanding. You will be involved from concept, all the way through deployment working closely with the team and taking part in the complete project life cycle, where you will have significant influence. Your work will lie in proposing and discovering novel methods that work well in practice and deploying them in the real world.
The role will entail designing and implementing a scalable workflow infrastructure, which will orchestrate the training and deployment of the distributed ML system - involving identifying bottlenecks and elaborating solutions. You will design and execute state-of-the-art experiments - including fast system prototyping - that have the potential to enhance or transform our overall system performance. Also, you will work alongside scientists and research engineers to contribute to technical blog posts, research papers, and open source implementations to further explore and research novel methods.
- Bachelor's Degree in Computer Science or related field
- Proficient in Unix environments, Python and Machine Learning libraries such as Tensorflow, Pytorch, Sklearn, pandas, NumPy
- Knowledge of software engineering practices and best practices for the full engineering (CI/CD, Docker, AWS etc.) life-cycle
- Understanding of design for scalability, performance and reliability of large-scale distributed ML systems
- Ability to produce well documented code that is fault-tolerant, efficient, and maintainable
Preferred skills (nice-to-haves):
- Master's Degree/PhD in Machine Learning, Computer Science, or related field
- Proficiency in other modern programming languages such as C++, Java, Scala
- 1-2+ years academic and/or industry experience working on Dialogue - Systems, NLP systems and/or large-scale Machine Learning
- Has produced demonstrable work in building real-world deployed machine learning/NLP systems
- Competent with large-scale data-serving and compute libraries and software, such as Hadoop, Spark, Apache Mesos, etc.