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For Companies At a Glance
- Tasks: Build and scale ML systems that reshape the media and entertainment industry.
- Company: Well-funded AI scale-up with a collaborative, senior team in London.
- Benefits: Competitive salary, significant equity, and a dynamic work environment.
- Other info: Join ahead of a major funding event for excellent career growth.
- Why this job: Make a real-world impact with cutting-edge technology in a visible industry.
- Qualifications: 5+ years in production ML systems, strong Python skills, and a product mindset.
The predicted salary is between 100000 - 155000 £ per year.
London (Hybrid) | £100K–£155K + Significant Equity
What if your work didn’t just scale models… but reshaped an entire industry? This is a well-funded AI scale-up (≈170 people, $100M+ raised) building cutting-edge technology used by some of the world’s most recognised media and entertainment organisations. Following rapid growth and new funding on the horizon, they’re hiring multiple Senior & Staff ML Systems Engineers to join a small, highly senior team in London.
This is not a research lab. And it’s not a pure platform role. It sits right in the middle — where production ML meets real-world impact at scale.
The Opportunity
- Build and scale infrastructure that enables training, evaluation, and deployment of advanced ML models
- Own end-to-end ML systems — from data ingestion to high-performance inference
- Work with large, complex datasets (including video and multimodal data)
- Optimise GPU workloads and parallelise models for production performance
- Develop tooling for experimentation, evaluation, and model lifecycle management
- Operate in a high-autonomy, low-ego, senior environment where engineering quality matters
This is a role for someone who enjoys getting their hands dirty — not just designing systems, but making them work at scale.
What we’re Looking For
- 5+ years building production ML systems OR ML infrastructure
- Strong Python skills + experience with a robust production language (C++, Java, Scala, etc.)
- Proven experience deploying and scaling models in production
- Solid understanding of distributed systems and performance optimisation
- Exposure to high-volume / large-format data (ideally vision or multimodal)
- A product mindset — you care about outcomes, not just elegant systems
Bonus points for:
- Experience with tools like Ray, Triton, Spark, Iceberg
- Background in startup environments (≈50–300 people)
Not the Right Fit If…
- You’re purely research-focused and prefer working on model development only
- You’ve only worked on internal platforms without owning production ML systems
- You thrive in highly structured big tech environments with limited ambiguity
Why This Role Stands Out
- Real-world impact at scale — your work is used globally in a highly visible industry
- Senior, low-ego team — you’ll be surrounded by people who genuinely know their craft
- Strong compensation — £100K–£155K base + equity (~50% of base)
- Timing matters — joining ahead of a major funding event
If you’re looking for a role where you can bridge ML research and real-world systems, and actually see your work in action at scale, this is one worth exploring.
Senior ML Systems Engineer in London employer: talentcentric.com
Contact Detail:
talentcentric.com Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Systems Engineer in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those already working at companies you're interested in. A friendly chat can open doors and give you insider info that could help you stand out.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repo showcasing your projects, especially those related to ML systems. This gives potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for interviews by brushing up on both technical and soft skills. Practice explaining complex concepts simply, as you'll need to communicate effectively with both techies and non-techies.
✨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, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Senior ML Systems Engineer in London
Some tips for your application 🫡
Show Your Passion for ML Systems: When writing your application, let us see your enthusiasm for building and scaling ML systems. Share specific examples of projects you've worked on that demonstrate your hands-on experience and how you’ve made an impact in previous roles.
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter to highlight the skills and experiences that align with our job description. We want to see how your background fits into the role of a Senior ML Systems Engineer, so don’t hold back on relevant details!
Be Clear and Concise: Keep your application clear and to the point. We appreciate well-structured applications that are easy to read. Use bullet points where necessary and avoid jargon unless it’s relevant to the role. Remember, clarity is key!
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 the role. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at talentcentric.com
✨Know Your ML Systems Inside Out
Make sure you’re well-versed in the specifics of production ML systems. Brush up on your experience with large-scale ML pipelines, and be ready to discuss how you've optimised GPU workloads or handled multimodal data in previous roles.
✨Showcase Your Problem-Solving Skills
Prepare to share examples of challenges you've faced in deploying and scaling models. Highlight your thought process and the steps you took to overcome these obstacles, as this will demonstrate your hands-on approach and product mindset.
✨Familiarise Yourself with Relevant Tools
If you have experience with tools like Ray, Triton, or Spark, make sure to mention it! Even if you haven't used them extensively, showing that you understand their purpose and potential impact can set you apart from other candidates.
✨Emphasise Team Collaboration
This role is all about working closely with researchers and engineers. Be prepared to discuss how you’ve collaborated in past projects, especially in high-autonomy environments. Highlight your ability to communicate effectively and contribute to a low-ego team dynamic.