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- Tasks: Build cutting-edge production inference systems using Python and GPUs.
- Company: Leading AI technology firm in Central London with a focus on innovation.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Why this job: Influence architectural decisions and shape the future of machine learning.
- Qualifications: Strong backend development skills and experience in API design.
- Other info: Join a dynamic team passionate about AI and technology.
The predicted salary is between 70000 - 90000 £ per year.
A leading AI technology firm is hiring an Engineering Lead for their ML Platform team in Central London. The role involves building production-grade inference systems using Python and GPUs, ensuring high standards for performance and reliability.
Ideal candidates will have:
- Strong backend development expertise
- Experience in API design
- A keen interest in machine learning model serving
This is an opportunity to influence architectural decisions and team direction in an innovative environment.
Senior ML Platform Engineer — Production Inference (Hybrid) employer: Workonomics
Contact Detail:
Workonomics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Platform Engineer — Production Inference (Hybrid)
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and ML community, especially those working at companies you're interested in. A friendly chat can open doors and give you insider info on job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to production-grade inference systems. This could be anything from GitHub repos to personal blogs explaining your work with Python and GPUs.
✨Tip Number 3
Prepare for technical interviews by brushing up on backend development and API design. Practice coding challenges and system design questions that are relevant to ML platforms to boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications!
We think you need these skills to ace Senior ML Platform Engineer — Production Inference (Hybrid)
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your backend development expertise and any experience you have with API design. We want to see how your skills align with building production-grade inference systems!
Be Specific About Your Experience: When detailing your past projects, focus on your work with Python and GPUs. We love seeing concrete examples of how you've tackled challenges in machine learning model serving.
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 Senior ML Platform Engineer role. It shows us you’re genuinely interested.
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 our team!
How to prepare for a job interview at Workonomics
✨Know Your Tech Inside Out
Make sure you brush up on your Python and GPU knowledge. Be ready to discuss your experience with backend development and API design in detail. They’ll want to see how you’ve tackled challenges in the past, so have some examples ready!
✨Show Your Passion for ML
Demonstrate your keen interest in machine learning model serving. Share any personal projects or experiences that highlight your enthusiasm for the field. This will show them that you’re not just looking for a job, but that you genuinely care about the technology.
✨Prepare for Architectural Discussions
Since this role involves influencing architectural decisions, be prepared to discuss your thoughts on system design. Think about scalability, performance, and reliability. They’ll appreciate candidates who can think critically about these aspects.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions! Inquire about the team’s current projects, challenges they face, or their vision for the ML Platform. This shows your interest in the role and helps you gauge if it’s the right fit for you.