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- Tasks: Contribute to exciting projects in autonomous driving and implement innovative research ideas.
- Company: Leading tech company in autonomous driving based in Greater London.
- Benefits: Competitive pay, hybrid work model, and expertise development in cutting-edge technologies.
- Why this job: Join a pioneering team and shape the future of autonomous driving technology.
- Qualifications: Python coding skills and experience with Deep Learning frameworks like TensorFlow or PyTorch.
- Other info: Perfect opportunity for Masters and PhD students looking to make an impact.
The predicted salary is between 500 - 1500 £ per month.
A leading autonomous driving technology company in Greater London is offering an internship that focuses on contributing to existing projects and implementing research ideas.
Candidates should have Python coding skills and experience in Deep Learning, specifically with frameworks like TensorFlow or PyTorch.
The internship is hybrid, with competitive compensation for Masters and PhD students, and opportunity to develop expertise in autonomous driving technologies.
Autonomous Driving ML Intern — Hybrid Internship employer: Waymo
Contact Detail:
Waymo Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Autonomous Driving ML Intern — Hybrid Internship
✨Tip Number 1
Network like a pro! Reach out to current or former interns at the company on LinkedIn. Ask them about their experiences and any tips they might have for landing the role.
✨Tip Number 2
Show off your skills! Prepare a mini-project or portfolio that highlights your Python coding and Deep Learning expertise. This can really set you apart during interviews.
✨Tip Number 3
Practice makes perfect! Brush up on common interview questions related to machine learning and autonomous driving. We recommend doing mock interviews with friends or using online platforms.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Autonomous Driving ML Intern — Hybrid Internship
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your Python coding skills and any experience you have with Deep Learning frameworks like TensorFlow or PyTorch. We want to see how your background aligns with our projects!
Tailor Your Application: Don’t just send a generic application! Take the time to tailor your CV and cover letter to reflect the specific requirements of the Autonomous Driving ML Intern role. It shows us you’re genuinely interested.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured applications that make it easy for us to see your qualifications and enthusiasm for the role.
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 this exciting opportunity in autonomous driving technology!
How to prepare for a job interview at Waymo
✨Brush Up on Your Python Skills
Make sure you're comfortable with Python coding, as it's a key requirement for the role. Review your past projects and be ready to discuss how you've used Python in your work, especially in relation to Deep Learning.
✨Know Your Frameworks Inside Out
Familiarise yourself with TensorFlow and PyTorch, as these are essential for the internship. Be prepared to explain the differences between them and share any personal experiences or projects where you've implemented these frameworks.
✨Research the Company’s Projects
Take some time to understand the company's current projects in autonomous driving. This will not only help you answer questions but also show your genuine interest in their work and how you can contribute.
✨Prepare Thoughtful Questions
Interviews are a two-way street, so think of insightful questions to ask about the team, the projects, and the company culture. This shows that you're engaged and serious about the opportunity.