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- Tasks: Design and deploy cutting-edge computer vision models in real-world environments.
- Company: Join a forward-thinking tech company focused on innovative machine learning solutions.
- Benefits: Competitive salary, hands-on experience, and opportunities for professional growth.
- Why this job: Make a tangible impact by working on live ML systems that solve real problems.
- Qualifications: 8+ years in AI/ML with strong production deployment experience.
- Other info: Collaborative team environment with a focus on knowledge-sharing and client engagement.
The predicted salary is between 54000 - 84000 £ per year.
My client is hiring for a senior, hands-on Machine Learning Engineering role focused on computer vision systems deployed into real-world environments. This role is technical and delivery-led. It would suit someone with strong applied coding and ML experience who has spent several years working in production, customer-facing environments, ideally with deep computer vision expertise and a clear track record of deploying, supporting, and iterating on live ML systems, rather than purely experimental or proof-of-concept work. If you come from a vehicle, manufacturing, or industrial automation background, with experience deploying computer vision or ML systems into live production environments, this would be a strong advantage.
Key Responsibilities
- Design, build, deploy, and iterate production-grade computer vision models
- Own ML systems end-to-end: from data and training through to deployment, monitoring, and iteration
- Deliver and support real customer deployments
- Collaborate closely with customers during deployment phases (requirements, constraints, trade-offs)
- Ensure models operate robustly in real-world conditions (changing data, environments, inputs)
- Integrate ML systems into existing customer environments, rather than greenfield platforms
- Contribute pragmatically within an experienced team, supporting collaboration and knowledge-sharing
Required Experience
- Strong AI / Machine Learning background with 8+ years of hands-on experience
- Clear evidence of production ML deployment (live, customer-facing systems)
- Experience deploying models that must perform reliably in real-time or near-real-time environments (e.g. PyTorch with ONNX or TorchScript, TensorRT, OpenCV)
- Comfortable owning deployed systems beyond initial launch
- Strong communication skills, including being client-facing when required
- Practical engineering judgement and a can-do mindset
Candidates must be happy to commute to Manchester on a weekly basis. Unfortunately, this role does not offer visa sponsorship, now or in the future.
Senior Machine Learning Engineer in Manchester employer: NearTech Search
Contact Detail:
NearTech Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer in Manchester
✨Tip Number 1
Network like a pro! Reach out to your connections in the machine learning and computer vision space. Attend meetups or webinars, and don’t be shy about asking for introductions. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those involving real-world deployments. We recommend including case studies that highlight your problem-solving abilities and the impact of your work. This will make you stand out when chatting with potential employers.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and ML concepts. We suggest doing mock interviews with friends or using online platforms. Being able to demonstrate your hands-on experience with tools like PyTorch or OpenCV will give you an edge!
✨Tip Number 4
Don’t forget to apply through our website! It’s a great way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive and engaged. So, get your application in and let’s get you that dream job!
We think you need these skills to ace Senior Machine Learning Engineer in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your hands-on experience with machine learning and computer vision. We want to see clear evidence of your production ML deployment, so don’t hold back on showcasing those real-world projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re the perfect fit for this role. Talk about your experience in customer-facing environments and how you've tackled challenges in deploying ML systems.
Showcase Your Technical Skills: Be specific about the tools and technologies you’ve used, like PyTorch, ONNX, or OpenCV. We love seeing practical engineering judgement, so mention any real-time or near-real-time environments you’ve worked in.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at NearTech Search
✨Know Your Stuff
Make sure you brush up on your machine learning and computer vision knowledge. Be ready to discuss specific projects you've worked on, especially those involving real-world deployments. Highlight your experience with tools like PyTorch, ONNX, and OpenCV.
✨Showcase Your Problem-Solving Skills
Prepare to talk about challenges you've faced in previous roles, particularly in deploying ML systems. Think of examples where you had to make tough decisions or trade-offs during deployment phases and how you ensured models operated robustly in changing environments.
✨Communicate Clearly
Since this role involves client-facing responsibilities, practice explaining complex technical concepts in simple terms. Be ready to demonstrate your strong communication skills by discussing how you've collaborated with customers in the past.
✨Demonstrate a Can-Do Mindset
Employers love candidates who show enthusiasm and a proactive attitude. Share instances where you took the initiative to improve processes or support your team. This will reflect your practical engineering judgement and readiness to own deployed systems beyond their initial launch.
