Machine Learning Scientist (Permanent - £100,000 salary plus excellent package, bonus, Travel allowance)
Hybrid working preferable (London office) but open to remote
Exciting opportunity for a Machine Learning Scientist to join a global organisation's Machine Learning Lab based in London. The companies Machine Learning Lab's mission is to support and empower the business units and partners with advanced technologies for Machine Learning.
The Machine Learning Lab covers the full research & development lifecycle, from explorative research to prototypes to industrial scale products. It typically undertakes projects with applications such as, but not limited to, analysis and description, creative tools, search, recommendation, auto-tagging etc.
As a Machine Learning Scientist, you will:
· Design and build state-of-the-art Audio and Machine Learning algorithms to tackle a variety of tasks.
· Explore cutting-edge research in ML/ Information Retrieval / Audio and new application avenues for the company.
· Collaborate with the team and stakeholders to deploy models to production.
· Communicate results and findings to audiences of all levels of expertise.
· Be part of an innovative and dynamic team
- Background in Information Retrieval / Machine Learning / Audio Signal Processing
- Solid successful experience in related field - e.g. 3+ years post PhD, or equivalent experience
- Experience in Information Retrieval tasks such as auto-tagging, classification, structural segmentation, or related tasks
- Up to date knowledge of general Machine Learning / Deep Learning state of the art
- Strong software engineering
- Thorough knowledge of Python and usual scientific libraries e.g. Numpy, Scipy, sklearn etc.
- Comprehensive knowledge and experience of Deep Learning and its usual frameworks (Tensorflow, Keras, Pytorch)
- Familiarity with Linux environments
- Ability to write good, well documented, and reusable code
- Track record of publication(s) in relevant conferences or journals (e.g. ISMIR, ICASSP, ICML, ICLR etc.)
- Experience with generative models
- Experience going all the way from ideation / early research to production.
- Familiarity/experience with cloud computing environments (e.g. AWS, Google Cloud Platform)
- Experience with containerised software development (Docker)