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- Tasks: Design and deploy machine learning models for cutting-edge engineering software.
- Company: Global software company focused on applied AI and high-performance tools.
- Benefits: Competitive salary, bonus, unrivalled benefits, and a collaborative work environment.
- Other info: Opportunity for long-term growth and collaboration with industry experts.
- Why this job: Tackle real-world engineering challenges with impactful AI solutions.
- Qualifications: Strong Python skills and experience with ML in practical applications.
The predicted salary is between 50000 - 70000 £ per year.
A global software company is evolving its core engineering platforms by embedding machine learning and applied AI into high-performance simulation and modelling tools used worldwide. This is a hands-on applied AI role focused on building and deploying ML solutions inside production-grade engineering systems, not isolated research or experimental prototypes.
You’ll design, build, and deploy machine learning models that directly enhance complex engineering software products. Expect a blend of ML engineering, software development, and computational problem solving. You’ll work across the full ML lifecycle, ensuring models are not only accurate, but efficient, scalable, and production-ready.
Key Responsibilities- Build and deploy ML models into production engineering software systems
- Own the full ML pipeline: data prep, feature engineering, training, evaluation, optimisation
- Translate complex scientific/engineering problems into ML-driven solutions
- Improve model performance in compute-intensive environments
- Write clean, testable, maintainable production code
- Integrate ML services via APIs and software components
- Collaborate with engineers and domain specialists on real-world systems
- Strong Python programming and software engineering fundamentals
- Proven experience applying ML to real-world datasets and problems
- Understanding of model trade-offs, performance, and production constraints
- Experience working with complex or imperfect data (not just curated datasets)
- Ability to write efficient, scalable, production-quality code
- PyTorch, TensorFlow, or similar ML frameworks
- Scientific computing / numerical methods / optimisation
- GPU acceleration or high-performance computing
- MLOps, model deployment, APIs, or production pipelines
- Focus on applied AI in real engineering systems
- Work on technically challenging, high-impact problems
- Close collaboration with experienced engineers and domain experts
- Influence how AI is embedded into core global software products
- Long-term technical depth, not short-cycle ML experimentation
Please send a copy of your CV to apply or call us for an informal chat. Thanks.
Machine Learning Engineer (Applied AI / Scientific Computing) employer: Ion recruitment
Contact Detail:
Ion recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (Applied AI / Scientific Computing)
✨Tip Number 1
Network like a pro! Reach out to professionals in the machine learning and engineering fields on LinkedIn. Join relevant groups, attend meetups, and don’t be shy about asking for informational interviews. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it’s GitHub repos or a personal website, having tangible examples of your work can really set you apart from the competition.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and ML concepts. Practice common algorithms and data structures, and be ready to discuss how you’ve applied ML to real-world problems. We recommend using platforms like LeetCode or HackerRank for practice.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love hearing from passionate candidates who are eager to dive into applied AI and make an impact in engineering systems.
We think you need these skills to ace Machine Learning Engineer (Applied AI / Scientific Computing)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning and software engineering. We want to see how you've tackled real-world problems, so don’t hold back on those specific projects that showcase your skills!
Showcase Your Skills: When writing your application, emphasise your Python programming and any experience with ML frameworks like PyTorch or TensorFlow. We’re looking for candidates who can demonstrate their technical prowess in a clear and concise manner.
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary. Make it easy for us to see why you’re a great fit for the role!
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 position. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at Ion recruitment
✨Know Your ML Fundamentals
Brush up on your machine learning fundamentals, especially around model performance and production constraints. Be ready to discuss how you've applied ML to real-world datasets and the trade-offs you've encountered.
✨Showcase Your Coding Skills
Prepare to demonstrate your Python programming skills. You might be asked to write clean, maintainable code during the interview, so practice coding challenges that focus on efficiency and scalability.
✨Understand the Full ML Lifecycle
Familiarise yourself with the entire ML pipeline from data preparation to model deployment. Be prepared to discuss specific examples of how you've owned this process in previous roles, particularly in high-performance environments.
✨Collaborate and Communicate
Highlight your experience working with engineers and domain specialists. Be ready to discuss how you translate complex problems into ML-driven solutions and how you collaborate effectively in a team setting.