At a Glance
- Tasks: Build scalable data pipelines for voice AI using machine learning techniques.
- Company: Join Synthesia, a leading innovator in media content creation based in Greater London.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Dynamic R&D environment with a focus on innovation and collaboration.
- Why this job: Be part of a team that’s redefining how media is created with cutting-edge technology.
- Qualifications: 3+ years in data engineering, strong Python skills, and familiarity with audio tech.
The predicted salary is between 50000 - 60000 £ per year.
Synthesia, based in Greater London, is seeking an experienced Machine Learning Data Engineer to join its R&D team. The role involves developing scalable data pipelines using machine learning techniques and requires a background in Computer Science or Engineering with at least 3 years of experience.
You will work with large audio and text datasets, ensuring data quality and accessibility. Experience with Python, Unix commands, and familiarity with audio technologies are essential.
Join us to help redefine media content creation!
ML Data Engineer for Scalable Voice AI Pipelines employer: Synthesia
At Synthesia, we pride ourselves on fostering a dynamic and innovative work culture that empowers our employees to push the boundaries of technology in the heart of Greater London. With a strong focus on professional development, we offer numerous growth opportunities and encourage collaboration within our R&D team, making it an exciting place for those passionate about machine learning and voice AI. Join us to be part of a forward-thinking company that values creativity and offers a unique chance to redefine media content creation.
StudySmarter Expert Advice🤫
We think this is how you could land ML Data Engineer for Scalable Voice AI Pipelines
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Synthesia. A friendly chat can sometimes lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your work with scalable data pipelines and machine learning projects. This will give you an edge and demonstrate your hands-on experience.
✨Tip Number 3
Prepare for the interview by brushing up on your Python and Unix commands. Be ready to discuss how you've tackled data quality issues in past projects—real examples go a long way!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the team.
We think you need these skills to ace ML Data Engineer for Scalable Voice AI Pipelines
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience with Python and Unix commands in your application. We want to see how you've used these skills in real projects, especially when working with audio and text datasets.
Tailor Your CV:Don’t just send the same CV for every job! Tailor it to reflect your experience relevant to the ML Data Engineer role. We love seeing how your background in Computer Science or Engineering aligns with what we’re looking for.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about scalable voice AI pipelines and how you can contribute to our R&D team. We appreciate a personal touch!
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 don’t miss out on any important updates from us!
How to prepare for a job interview at Synthesia
✨Know Your Tech Inside Out
Make sure you brush up on your Python skills and Unix commands. Be ready to discuss specific projects where you've implemented these technologies, especially in relation to audio datasets. The more you can demonstrate your technical prowess, the better!
✨Showcase Your Problem-Solving Skills
Prepare to talk about challenges you've faced in previous roles, particularly around data quality and accessibility. Think of examples where you used machine learning techniques to overcome obstacles. This will show them you're not just a coder, but a thinker!
✨Familiarise Yourself with Audio Technologies
Since the role involves working with audio data, it’s crucial to have a good grasp of relevant audio technologies. Research common tools and frameworks used in the industry, and be ready to discuss how you've used them or how you would approach using them.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about their current projects, team dynamics, and future goals. This shows your genuine interest in the role and helps you assess if it's the right fit for you.
