At a Glance
- Tasks: Build and scale AI infrastructure for cutting-edge multimodal models.
- Company: Fast-growing AI company with over £100m raised.
- Benefits: Competitive salary up to £150k, hybrid work, and strong career growth.
- Other info: Collaborate directly with scientists and shape the future of AI.
- Why this job: Join a small team and make a real impact in AI systems.
- Qualifications: Experience in ML systems engineering and strong Python skills required.
The predicted salary is between 135000 - 150000 £ per year.
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 Slough 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 innovation and ownership. Benefit from competitive compensation, significant backing, and the opportunity to shape the future of AI technology.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer - Systems in Slough
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML systems engineering. This is your chance to demonstrate your expertise in Python, PyTorch, and distributed systems.
✨Tip Number 3
Prepare for technical interviews by brushing up on your problem-solving skills. Practice coding challenges and system design questions that are relevant to ML infrastructure. We want you to feel confident when it’s showtime!
✨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 take the initiative to connect directly with us.
We think you need these skills to ace Machine Learning Engineer - Systems in Slough
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. We want to see how you can contribute to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about ML systems and how your experience aligns with what we're looking for. Be genuine and let your personality come through – we love seeing the real you!
Showcase Your Projects:If you've worked on any interesting projects related to distributed systems or large-scale datasets, make sure to mention them. We’re keen to see practical examples of your problem-solving skills and how you’ve tackled challenges in the past.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our awesome team at StudySmarter!
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 the industry. Being able to articulate how your skills align with their goals will show that you’re genuinely interested and invested in their mission.
✨Ask Insightful Questions
Prepare thoughtful questions about the team dynamics, the challenges they face, and the future direction of their ML systems. This not only demonstrates your interest but also helps you gauge if the company is the right fit for you.
