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- Tasks: Join our team to develop cutting-edge algorithms for human-like speech technology.
- Company: Be part of Amazon, a leader in innovation and technology.
- Benefits: Enjoy a collaborative environment with access to vast resources and data.
- Why this job: Make a real impact on customer experience through advanced speech technology.
- Qualifications: PhD or Master's in relevant fields; programming skills in Java, C++, or Python required.
- Other info: Opportunity to mentor junior talent and work in beautiful Cambridge, UK.
The predicted salary is between 43200 - 72000 £ per year.
We are the Text-to-Speech Research team and our goal is to improve the core technology to improve Alexa’s voice and make it more human-like and expressive.
We are looking for a passionate, talented, and inventive Scientist with a strong background in Machine/Deep Learning to join us in beautiful Cambridge, UK. Our mission is to push the envelope in computer-generated speech in order to provide the best-possible experience for our customers
As an applied scientist, you will work with talented peers to develop novel algorithms and modelling techniques to advance the state-of-the-art in spoken language generation. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in computer generated speech.
Position Responsibilities:
– Research and implement novel Machine/Deep Learning approaches which add value to Amazon
– Lead and Mentor junior engineers and scientists
– Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for spoken language applications
– Develop and/or apply statistical modelling methods (e.g. deep neural networks), optimizations, and other ML techniques to different applications in spoken language engineering
Company:
Amazon
Qualifications:
BASIC QUALIFICATIONS
– PhD, or a Master’s degree and experience in CS, CE, ML or related field
– Experience in patents or publications at top-tier peer-reviewed conferences or journals
– Experience programming in Java, C++, Python or related language
– Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
– Experience in building machine learning models for business application
PREFERRED QUALIFICATIONS
– Experience using Unix/Linux
– Experience in professional software development
Educational level:
Master Degree
Tagged as: Industry, Machine Learning, Master Degree, Parsing, Text-To-Speech, United Kingdom
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Deep learning scientist employer: NLP PEOPLE
Contact Detail:
NLP PEOPLE Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Deep learning scientist
✨Tip Number 1
Familiarise yourself with the latest advancements in deep learning and natural language processing. Follow relevant research papers, attend webinars, and engage with online communities to stay updated on trends that could enhance your understanding and skills.
✨Tip Number 2
Network with professionals in the field of machine learning and speech technology. Attend conferences or local meetups where you can connect with industry experts, as personal connections can often lead to job opportunities.
✨Tip Number 3
Showcase your expertise by contributing to open-source projects related to deep learning or text-to-speech technologies. This not only builds your portfolio but also demonstrates your commitment and passion for the field.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and algorithm problems, especially those related to machine learning. Familiarity with common interview questions in this domain will boost your confidence and performance.
We think you need these skills to ace Deep learning scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in Machine/Deep Learning, programming languages like Python, and any relevant projects or publications. Emphasise your educational background, especially if you have a PhD or Master's degree in a related field.
Craft a Compelling Cover Letter: In your cover letter, express your passion for improving speech technology and how your skills align with the role. Mention specific experiences that demonstrate your ability to develop novel algorithms and mentor others.
Showcase Relevant Projects: Include details about any projects you've worked on that relate to spoken language generation or machine learning. Highlight your contributions and the impact of these projects, especially if they involved large-scale data or innovative techniques.
Prepare for Technical Questions: Anticipate technical questions related to algorithms, data structures, and machine learning models. Be ready to discuss your problem-solving approach and any challenges you've faced in previous projects.
How to prepare for a job interview at NLP PEOPLE
✨Showcase Your Research
Be prepared to discuss your previous research and any publications or patents you've contributed to. Highlight how your work aligns with the goals of the Text-to-Speech Research team and demonstrate your understanding of the latest advancements in machine learning.
✨Demonstrate Technical Proficiency
Make sure you can confidently discuss your programming skills, particularly in Java, C++, and Python. Be ready to explain your experience with algorithms, data structures, and any relevant machine learning models you've built, especially those applicable to spoken language applications.
✨Prepare for Problem-Solving Questions
Expect to face technical questions that assess your problem-solving abilities. Practice explaining your thought process when tackling complex problems, particularly in areas like numerical optimisation and parallel computing, as these are crucial for the role.
✨Emphasise Team Collaboration
Since the role involves mentoring junior engineers and collaborating with peers, be sure to highlight your teamwork experiences. Share examples of how you've successfully led projects or supported colleagues in a research environment, showcasing your ability to contribute to a positive team dynamic.
