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- Tasks: Lead the development of advanced AI models and optimise training pipelines.
- Company: Innovative AI company focused on cutting-edge technology.
- Benefits: Hybrid or remote work, competitive salary, and opportunities for professional growth.
- Why this job: Join a team pushing the boundaries of AI and make a real-world impact.
- Qualifications: 3-5 years in ML engineering with strong skills in PyTorch and model training.
- Other info: Collaborative environment that values innovation and experimentation.
The predicted salary is between 36000 - 60000 £ per year.
We’re looking for an experienced Machine Learning Engineer to lead the development and training of advanced large-scale language models. In this role, you will be responsible for pushing the performance and reliability of next-generation AI systems, specifically focusing on models that assist with complex real-world tasks. You’ll work closely with cross-functional teams including infrastructure, product and research to shape both the training pipeline and the evaluation of highly capable models.
Key Responsibilities
- Design and execute large-scale training experiments on multi-GPU and distributed environments using cutting-edge ML frameworks.
- Lead both supervised fine-tuning (SFT) and reinforcement learning (RL) workflows to improve model performance on domain-specific tasks.
- Build, maintain, and optimise custom training pipelines, including dataset preparation, distributed training primitives, and scheduling of multi-node jobs.
- Collaborate across engineering and research teams to align training goals with product priorities and performance metrics.
- Troubleshoot training challenges such as stability, scaling issues, and GPU utilisation bottlenecks.
What We’re Looking For
- Experience: 3–5+ years working in ML engineering or applied machine learning roles, with hands-on responsibility for training and deploying models in production-like environments.
- Technical Skills:
- Strong proficiency with PyTorch including distributed training (e.g., DDP/FSDP).
- Practical experience training large sequence models or transformer-based architectures.
- Comfortable building and maintaining data pipelines, optimising large datasets, and handling model scaling challenges.
- Solid software engineering fundamentals — clean, maintainable code and version control best practices.
Desirable Qualities
- Experience with reinforcement learning workflows and sequence-level reward strategies.
- Familiarity with model evaluation tools and benchmarks for large-scale AI systems.
- A proactive, collaborative mindset that thrives in a fast-moving environment where innovation and experimentation are central.
Machine Learning Engineer (hybrid or remote) in City of London employer: Block MB
Contact Detail:
Block MB Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (hybrid or remote) in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving large-scale language models or reinforcement learning. We want to see what you can do, so make it easy for us to find your best work.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and problem-solving abilities. We love candidates who can think on their feet, so practice coding challenges and be ready to discuss your past experiences in detail.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always looking for passionate individuals who are eager to join our team and push the boundaries of AI.
We think you need these skills to ace Machine Learning Engineer (hybrid or remote) in City of 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 experience with PyTorch, large-scale training, and any relevant projects you've worked on. We want to see how you fit into our team!
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 goals at StudySmarter. Keep it engaging and personal – we love to see your personality!
Showcase Your Projects: If you've worked on interesting ML projects, don’t hold back! Include links to your GitHub or any relevant portfolios. We’re keen to see your hands-on experience and how you tackle real-world challenges in ML.
Apply Through Our Website: We encourage you to apply directly through our website for a smoother process. It helps us keep track of applications and ensures you get the best chance to showcase your skills. Let’s make this happen together!
How to prepare for a job interview at Block MB
✨Know Your Models Inside Out
Make sure you’re well-versed in the latest advancements in large-scale language models. Be prepared to discuss your experience with PyTorch, especially around distributed training and transformer architectures. Brush up on any projects you've worked on that involved supervised fine-tuning or reinforcement learning.
✨Showcase Your Problem-Solving Skills
During the interview, be ready to tackle hypothetical scenarios related to training challenges. Think about how you would address issues like stability or GPU utilisation bottlenecks. Sharing specific examples from your past experiences can really highlight your troubleshooting abilities.
✨Collaboration is Key
Since this role involves working closely with cross-functional teams, emphasise your communication skills. Prepare to discuss how you’ve successfully collaborated with both technical and non-technical stakeholders in previous roles. This will show that you can align training goals with product priorities effectively.
✨Demonstrate Your Coding Practices
Be ready to talk about your coding standards and version control practices. Bring examples of clean, maintainable code you’ve written, and discuss how you optimise data pipelines. This will demonstrate your solid software engineering fundamentals and your ability to handle large datasets.
