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- Tasks: Build and operate production ML systems to detect disinformation.
- Company: Join a forward-thinking tech company focused on impactful solutions.
- Benefits: Competitive salary, equity options, generous holiday, and remote flexibility.
- Other info: Enjoy high ownership and excellent career growth opportunities in a dynamic environment.
- Why this job: Make a real-world impact while working with cutting-edge machine learning technologies.
- Qualifications: Experience in production ML, strong coding skills, and familiarity with data systems.
The predicted salary is between 130000 - 150000 £ per year.
Join as a Senior ML Engineer to build production ML systems for detecting disinformation, working with a cross-functional team in a high-ownership role.
ABOUT THE ROLE
You’ll be building and operating production-grade machine learning systems that detect and counter disinformation as it happens. This is a high-ownership role where you’ll work across the full ML lifecycle — from model development to deployment and monitoring. You’ll collaborate with a cross-functional team spanning engineering, machine learning, and intelligence backgrounds, shaping both the product and the underlying ML infrastructure. This is not a research-only role — the focus is on real-world systems that are reliable, scalable, and fast.
TECH STACK
Python, SQL & NoSQL databases, streaming & batch data processing frameworks, Docker, CI/CD pipelines, cloud infrastructure.
WHAT WE LOOK FOR
- Production ML experience: Experience building and deploying machine learning systems in production environments.
- Strong engineering fundamentals: Comfortable writing clean, modular, maintainable code that others can build on.
- End-to-end ownership: Able to manage the full lifecycle — from data and models to deployment, monitoring, and iteration.
- Algorithmic versatility: Broad exposure across ML approaches and the ability to apply the right method to the problem.
- Data systems experience: Solid understanding of both relational and non-relational databases, as well as large-scale data processing.
- Systems thinking: You understand trade-offs — latency vs accuracy, cost vs performance — and design accordingly.
- Infrastructure familiarity: Hands-on experience with containerisation, CI/CD, and production environments.
COMPENSATION & BENEFITS
- Salary: £130,000 – £150,000 depending on experience
- Equity: 0.25–0.5% early-stage share options
- Time off: 25 days holiday + your birthday off
- Pension: Private pension contributions
- Flexibility: Remote-first with flexible working and an outcomes-driven culture
- Perks: Early employee equity and high ownership from day one
Senior Machine Learning Engineer in London employer: Talent Search PRO
Contact Detail:
Talent Search PRO Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those that demonstrate your experience with production systems. This will give you an edge and show that you can walk the walk, not just talk the talk.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and approach real-world problems!
✨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 our team at StudySmarter.
We think you need these skills to ace Senior Machine Learning Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your production ML experience and engineering fundamentals. We want to see how your skills align with the role, so don’t be shy about showcasing your algorithmic versatility and hands-on experience with CI/CD pipelines.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about building production-grade ML systems with us. Share specific examples of your past work that demonstrate your end-to-end ownership and systems thinking.
Showcase Your Tech Stack Knowledge: We’re keen on seeing your familiarity with our tech stack. Mention your experience with Python, SQL, NoSQL databases, and any streaming or batch data processing frameworks you’ve worked with. This will help us understand how you can hit the ground running!
Apply Through Our Website: Don’t forget to apply 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 at StudySmarter!
How to prepare for a job interview at Talent Search PRO
✨Know Your Tech Stack Inside Out
Make sure you’re well-versed in Python, SQL, NoSQL databases, and the various data processing frameworks mentioned in the job description. Brush up on your knowledge of Docker and CI/CD pipelines, as these are crucial for the role. Being able to discuss your hands-on experience with these technologies will show that you’re ready to hit the ground running.
✨Showcase Your Production ML Experience
Prepare specific examples of machine learning systems you've built and deployed in production environments. Be ready to discuss the challenges you faced and how you overcame them. This will demonstrate your end-to-end ownership and understanding of the full ML lifecycle, which is key for this position.
✨Understand Systems Thinking
Familiarise yourself with the trade-offs involved in machine learning, such as latency versus accuracy and cost versus performance. Be prepared to discuss how you’ve made design decisions based on these trade-offs in past projects. This will highlight your ability to think critically about system design.
✨Collaborate and Communicate
Since this role involves working with a cross-functional team, practice articulating your ideas clearly and concisely. Think about how you can effectively communicate complex technical concepts to non-technical team members. This will show that you can collaborate well and contribute to shaping both the product and the underlying ML infrastructure.