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- Tasks: Design and scale ML models for product matching and personalisation.
- Company: Innovative global tech marketplace focused on diversity and sustainability.
- Benefits: Flexible hybrid work, private medical insurance, and generous parental leave.
- Why this job: Lead impactful projects and mentor others while shaping the future of technology.
- Qualifications: Experience in ML with strong Python skills and knowledge of data engineering.
- Other info: Great opportunities for learning, growth, and collaboration in a dynamic environment.
The predicted salary is between 48000 - 72000 £ per year.
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Overview
Job title: Senior Machine Learning Scientist
Job type: Permanent
Role Location: London (Hybrid)
The company:
An innovative, global tech-driven marketplace, connecting millions of users and powered by cutting-edge data and engineering. The company values diversity, sustainability, and community while driving forward new approaches to technology and customer experience.
Role and responsibilities
- Design, build, and scale core ML models for applications such as product matching, content understanding, moderation, and personalisation.
- Lead high-impact technical projects, set modelling direction, and mentor junior team members.
- Collaborate with product managers, engineers, and other scientists across the business.
- Conduct large-scale experiments and A/B tests with statistical rigour.
- Stay up to date with ML research, contribute to knowledge sharing, and help shape long-term strategy.
- Communicate complex technical concepts to both technical and non-technical audiences.
Job requirements
- Proven track record as a Machine Learning Scientist delivering models into production.
- Expertise with frameworks such as PyTorch, TensorFlow, or Transformers.
- Strong Python skills with clean, production-ready coding practices.
- Solid knowledge of data engineering and MLOps principles.
- Ability to lead end-to-end ML projects and mentor colleagues.
- Excellent communication and collaboration skills.
Bonus skills (not essential)
- Experience with NLP, image classifiers, deep learning, or large language models.
- Familiarity with A/B testing and experiment design.
- Experience building shared ML systems or platforms.
- Knowledge of Databricks, PySpark, and cloud platforms (AWS/GCP/Azure).
- Flexible hybrid working model.
- Private medical insurance, healthcare cash plan, subsidised counselling/coaching, Employee Assistance Programme, and Mental Health First Aiders.
- Family Support: 18 weeks paid parental leave, plus IVF, shared parental, and emergency carer leave.
- Learning & Growth: Conference and learning budgets, mentorship, and upskilling programmes.
If you\’d like to find out more, don\’t hesitate to apply! Even if you don\’t tick every box!
Seniority level
- Mid-Senior level
Employment type
- Full-time
Job function
- Software Development
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Senior Machine Learning Engineer employer: Signify Technology
Contact Detail:
Signify Technology Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to current employees on LinkedIn or attend industry meetups. We can’t stress enough how personal connections can give you the inside scoop on job openings and company culture.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those using PyTorch or TensorFlow. We love seeing real-world applications of your work, so make it easy for potential employers to see what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and ML concepts. We recommend practicing with mock interviews or coding challenges to build confidence and get familiar with common questions.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always looking for passionate individuals who want to make an impact in the tech world.
We think you need these skills to ace Senior Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Senior Machine Learning Engineer role. Highlight your expertise with frameworks like PyTorch and TensorFlow, and don’t forget to showcase any leadership experience you have!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how your background aligns with our innovative approach. Keep it engaging and personal – we want to get to know you!
Showcase Your Projects: If you've worked on any relevant ML projects, make sure to include them in your application. Whether it's product matching or A/B testing, we love seeing real-world applications of your skills. Don’t be shy about sharing your successes!
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be one step closer to joining our amazing team at StudySmarter!
How to prepare for a job interview at Signify Technology
✨Know Your ML Models Inside Out
Make sure you can discuss your experience with various machine learning models and frameworks like PyTorch and TensorFlow. Be ready to explain how you've designed, built, and scaled models in the past, especially for applications like product matching or personalisation.
✨Showcase Your Leadership Skills
Since this role involves leading high-impact projects and mentoring junior team members, prepare examples of how you've successfully led teams or projects before. Highlight your ability to set direction and collaborate effectively with product managers and engineers.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You might be asked to communicate your ideas to non-technical audiences, so being able to break down your work will show your versatility and understanding of the subject matter.
✨Stay Current with ML Trends
Demonstrate your passion for machine learning by discussing recent research or trends in the field. Mention any contributions you've made to knowledge sharing or how you've applied new findings to your work, as this shows you're proactive and engaged in the community.