High-Performance Machine Learning Engineer
6 days ago
A challenging opportunity has arisen for a seasoned PhD Research Scientist to join our Personalization team in Edinburgh, Scotland.
Estimated Salary Range:£80,000 - £110,000 per annum, depending on experience.
About the RoleWe're seeking an expert in machine learning and large models to develop and disseminate customer-facing personalized recommendation models. As a key member of our team, you will be responsible for designing and executing experiments to demonstrate the technical efficacy and business value of your methods.
You will work closely with software engineers to integrate successful experimental results into large-scale production systems capable of handling high volumes of transactions at low latency. Additionally, you will lead the design of machine learning models that scale to very large quantities of data and serve high-scale low-latency recommendations to all customers worldwide.
Key Responsibilities- Develop machine learning algorithms for high-scale recommendations problem.
- Rapidly design, prototype, and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgment.
- Collaborate with software engineers to integrate successful experimental results into large-scale production systems.
- Report results in a manner which is both statistically rigorous and compellingly relevant.
To be successful in this role, you will need a PhD in machine learning or a related field, along with proven leadership experience in generative AI, NLP, and/or large models. You should also have experience programming in languages such as Java, C++, or Python, and a track record of building machine learning models for business applications.
Dream Candidate RequirementsWe are looking for someone with expertise in modeling tools like PyTorch, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, and scipy. Additionally, experience in large-scale distributed systems such as Hadoop and Spark would be highly beneficial.
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