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- Tasks: Build and maintain NLP systems for healthcare document processing.
- Company: Exciting health-tech startup in Greater London with a focus on innovation.
- Benefits: Hybrid working model, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment that fosters creativity and teamwork.
- Why this job: Join a dynamic team and make a real difference in healthcare technology.
- Qualifications: 2+ years in applied NLP, machine learning, and strong Python skills.
The predicted salary is between 36000 - 60000 £ per year.
A developing health-tech startup in Greater London is seeking an experienced NLP Engineer to join their Applied AI team. This hands-on engineering role focuses on building and maintaining NLP systems that address healthcare document processing needs.
The candidate should have at least 2 years of experience in applied NLP and machine learning, along with strong Python programming skills.
The position is offered on a hybrid working model, enabling productive teamwork in an innovative environment.
Clinical NLP Engineer — Production ML & OCR employer: Dyad AI, Inc.
Contact Detail:
Dyad AI, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Clinical NLP Engineer — Production ML & OCR
✨Tip Number 1
Network like a pro! Reach out to people in the health-tech and NLP space on LinkedIn. A friendly message can go a long way, and you never know who might have the inside scoop on job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your NLP projects or contributions to open-source. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Brush up on your Python skills and be ready to discuss your experience with machine learning and NLP systems. Practising common interview questions can help you feel more confident.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Clinical NLP Engineer — Production ML & OCR
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in NLP and machine learning. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about healthcare tech and how your background makes you a perfect fit for our Applied AI team. Keep it engaging and personal!
Showcase Your Python Skills: Since strong Python programming skills are a must, consider including specific examples of projects where you've used Python for NLP tasks. We love seeing practical applications of your coding prowess!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Dyad AI, Inc.
✨Know Your NLP Inside Out
Make sure you brush up on your knowledge of natural language processing. Be ready to discuss specific projects you've worked on, the challenges you faced, and how you overcame them. This will show your hands-on experience and problem-solving skills.
✨Show Off Your Python Skills
Since strong Python programming skills are a must, be prepared to demonstrate your coding abilities. You might be asked to solve a problem on the spot or explain your thought process behind a piece of code. Practising common algorithms and libraries used in NLP can give you an edge.
✨Understand the Healthcare Context
Familiarise yourself with the healthcare document processing landscape. Knowing the specific challenges and regulations in this field will help you tailor your answers and show that you're genuinely interested in the role and its impact.
✨Embrace the Hybrid Model
Since the position offers a hybrid working model, think about how you can contribute to teamwork in both remote and in-person settings. Be ready to share examples of how you've successfully collaborated with teams in different environments, highlighting your adaptability.