At a Glance
- Tasks: Build and scale infrastructure for cutting-edge AI systems and optimise ML workflows.
- Company: Fast-growing AI company with over £100m raised, focused on innovative solutions.
- Benefits: Competitive salary up to £150k, hybrid work model, and opportunities for professional growth.
- Other info: Collaborate directly with scientists and shape the future of AI technology.
- Why this job: Join a small, senior team and make a real impact in the AI landscape.
- Qualifications: Strong background in ML systems engineering and experience with Python and distributed systems.
The predicted salary is between 150000 - 150000 £ per year.
Senior / Staff ML Systems Engineer
London (Hybrid)
Up to £150k
Well-funded AI company (£100m+ raised)
We're working with a fast-growing AI company building production systems for training and deploying cutting-edge multimodal models across video, embeddings, and large-scale metadata.
You'll join a small, highly technical team owning the ML infrastructure and systems layer end-to-end — from large-scale data ingestion and training pipelines through to high-performance production inference.
What you'll be doing:
- Building and scaling infrastructure for massive multimodal datasets
- Improving distributed ML training systems using PyTorch and Ray
- Developing tooling for experimentation, evaluation, and dataset analysis
- Owning model lifecycle workflows across training, deployment, and rollout
- Optimising GPU inference systems for performance, latency, and reliability
What they're looking for:
- Strong background in ML systems engineering or production ML infrastructure
- Experience deploying ML models into real-world production environments
- Strong Python skills plus experience with a systems language (C++ / Java etc.)
- Experience working with distributed systems and large-scale datasets
- Engineers who enjoy solving practical problems end-to-end rather than purely research work
Tech:
- Python, PyTorch, Ray
- Distributed systems & GPU infrastructure
- Large-scale data platforms
- ML serving & inference systems
Why it's interesting:
- Real-world AI systems operating at significant scale
- High ownership within a small, senior engineering team
- Direct collaboration with applied scientists and research teams
- Strong commercial traction and significant backing
- Opportunity to help shape the next stage of platform growth
Machine Learning Engineer - Systems in London employer: Atarus
Join a dynamic and well-funded AI company in London, where you'll be part of a small, highly skilled team dedicated to building and optimising cutting-edge machine learning systems. With a strong focus on employee growth and collaboration, this role offers the chance to work on real-world AI applications while enjoying a hybrid work culture that promotes flexibility and innovation. Benefit from competitive compensation, significant backing, and the opportunity to make a meaningful impact in the rapidly evolving field of AI.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer - Systems in London
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and ML community on LinkedIn or at meetups. 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 projects, especially those involving ML systems or large-scale datasets. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and systems languages. Practice coding challenges and be ready to discuss your experience with distributed systems and GPU infrastructure. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Machine Learning Engineer - Systems in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your background in ML systems engineering and any relevant projects you've worked on, especially those involving Python, PyTorch, or distributed systems.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about machine learning and how your experience aligns with our needs. Share specific examples of how you've tackled challenges in ML infrastructure or production environments.
Showcase Your Technical Skills:Don’t shy away from detailing your technical expertise! Mention your proficiency in Python and any systems languages you know, as well as your experience with GPU inference systems and large-scale datasets. We love seeing practical problem-solving skills!
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 gives you a chance to explore more about our company culture and values!
How to prepare for a job interview at Atarus
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, PyTorch, and Ray. Brush up on your knowledge of distributed systems and GPU infrastructure, as these will likely come up during technical discussions.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've tackled practical problems in ML systems engineering. Think about challenges you've faced with large-scale datasets or model deployment, and be ready to explain your thought process and solutions.
✨Understand the Company’s Vision
Research the AI company’s projects and their impact on real-world applications. Being able to articulate how your skills align with their goals will demonstrate your genuine interest and help you stand out as a candidate who’s invested in their mission.
✨Ask Insightful Questions
Prepare thoughtful questions that show your curiosity about the role and the team dynamics. Inquire about their current challenges in ML infrastructure or how they measure success in model deployment. This not only shows your engagement but also helps you assess if the company is the right fit for you.
