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- Tasks: Build and own the recommendations infrastructure from the ground up.
- Company: Fast-growing startup in Greater London with a focus on innovation.
- Benefits: Competitive salary, growth opportunities, and a dynamic work environment.
- Why this job: Make a real impact by embedding ML into user experiences.
- Qualifications: 5+ years in machine learning, especially in ranking and recommendations.
- Other info: Collaborate with Data, Product, and Engineering teams in an exciting startup.
The predicted salary is between 48000 - 72000 £ per year.
A fast-growing startup in Greater London seeks a Senior Machine Learning Engineer to establish the recommendations infrastructure from scratch. This role offers significant ownership, working closely with Data, Product, and Engineering teams to embed ML capabilities into the user experience.
Ideal candidates have over 5 years of experience in machine learning, particularly in ranking and recommendation systems, and are proficient in Python and cloud-based ML technologies like GCP. A competitive package and opportunity for growth await.
Senior ML Engineer: Ranking & Recommendations, Own ML Infra in London employer: trg.recruitment
Contact Detail:
trg.recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Engineer: Ranking & Recommendations, Own ML Infra in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at startups. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those related to ranking and recommendations. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and cloud-based ML technologies. Practice coding challenges and be ready to discuss your past projects in detail.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you, and applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Senior ML Engineer: Ranking & Recommendations, Own ML Infra in London
Some tips for your application 🫡
Show Off Your Experience: Make sure to highlight your 5+ years of experience in machine learning, especially with ranking and recommendation systems. We want to see how your background aligns with the role, so don’t hold back on those relevant projects!
Get Technical: Since we’re looking for someone proficient in Python and cloud-based ML technologies like GCP, be sure to mention your technical skills clearly. We love seeing specific examples of how you've used these tools in your previous roles.
Emphasise Collaboration: This role involves working closely with Data, Product, and Engineering teams, so let us know about your teamwork experiences. Share instances where you’ve successfully collaborated to embed ML capabilities into user experiences.
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 trg.recruitment
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts, especially around ranking and recommendation systems. Be ready to discuss your past projects in detail, showcasing how you've built or improved these systems.
✨Showcase Your Python Skills
Since proficiency in Python is key for this role, prepare to demonstrate your coding skills. You might be asked to solve a problem on the spot, so practice common algorithms and data structures relevant to ML.
✨Familiarise with Cloud Technologies
As the job involves cloud-based ML technologies like GCP, make sure you understand how to deploy models in the cloud. Be prepared to discuss your experience with cloud infrastructure and any challenges you've faced.
✨Collaboration is Key
This role requires working closely with Data, Product, and Engineering teams. Think of examples where you've successfully collaborated across teams, and be ready to explain how you can contribute to embedding ML capabilities into user experiences.