Staff ML Engineer / Eng Manager / Director - Recommender Systems
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| Area | UK WIDE |
| Sector | Machine Learning |
| Job Ref | 1207137 |
- Description
- Role: Senior / Staff / Principal Machine Learning Engineer (IC / Eng Managers / Tech Leads / Directors)
Specialism: Recommender Systems
Location: Remote - Europe (You can be based anywhere across Europe)
We’re partnered with a global giant who are a challenger to some of the major social media platforms and they are looking to add creative and self-driven Staff or Principal Machine Learning Engineers to work on various projects within Recommender Systems.
To give you insight into the role, they currently support close to 250 million users on their platform who have come online in just the last few years and their vision for the product is to revolve around its algorithmically generated content feeds and ensuring users remain engaged and diverse as it grows on a massive scale.
From a team perspective, they operate in a free and experimental way, the major difference from their competitors is they allow staff & engineer’s complete freedom to conduct experiments by learning from both successes and failures to develop highly scalable and state-of-the-art algorithms that will be used by hundreds of millions of people globally.
We’re currently attracting subject matter experts from Facebook, Amazon, Apple & Spotify, and we’re keen to hear from candidates who’ve worked on similar projects scaling up large systems (all applications considered).
Responsibilities
- Recommender systems development experience, you must be able to display proven examples of building architecture from the ground up at a massive scale
- Work in close partnership with product and data teams to identify new opportunities to drive recommendation strategy
- Develop highly scalable algorithms based on state-of-the-art machine learning techniques
- Design, implement, and continuously optimise data pipelines for our ML recommendation systems
- Apply state of the art in Recommender Systems techniques to make significant contributions
- Apply best practices in big data processing to build feature stores, data pipelines and model inference services that can deal with massive scale.
- Continually improve the recommendations we provide users and deliver the best experience to our customer base
- Ph.D degree in Computer Science or related quantitative discipline
- Experience in training ML models with frameworks like Tensorflow, PyTorch, MXNet, Caffe, Torch etc.
- Excellent coding skills in at least one of Python, C/C++, Java, NodeJS, Go, Scala
- Experience with distributed systems, scalable data processing frameworks (e.g., Spark,Kafka) and noSQL systems (e.g., HBase, Cassandra) is a plus
As mentioned, we have a variety of hands-on and off positions so feel free to drop me a message (Chris Coyne) on LinkedIn to learn more if you are from a strong recommenders background.
