At a Glance
- Tasks: Develop and deploy cutting-edge machine learning models for computer vision applications.
- Company: Join a tech company revolutionising automation with innovative AI solutions.
- Benefits: Enjoy flexible work options, equity shares, generous leave, and comprehensive health cover.
- Why this job: Work on impactful projects in a collaborative culture that values innovation and inclusivity.
- Qualifications: Master’s degree in a relevant field and 3+ years of ML model deployment experience required.
- Other info: Opportunity to attend retreats and team events while working in a hybrid setup.
The predicted salary is between 48000 - 72000 £ per year.
Overview: We’re looking for a skilled Machine Learning Engineer to join a growing technology company building cutting-edge solutions for real-world automation. You’ll be part of a small, collaborative team applying computer vision to improve performance, efficiency, and user experience across multiple sectors. This role offers the chance to work on high-impact machine learning problems, shape production-ready models, and contribute to the development of a platform that’s democratising access to AI-driven automation.
Key Responsibilities:
- Model Development: Design, train, and deploy machine learning models for computer vision use cases such as object detection, classification, and segmentation.
- Data Handling: Collaborate with data engineers to manage large datasets, ensuring quality data pipelines for model training and evaluation.
- Algorithm Tuning: Optimise model performance through experimentation with architectures and hyperparameters.
- Cross-Functional Collaboration: Work closely with engineers, product managers, and designers to integrate ML solutions into customer-facing applications.
- Monitoring & Maintenance: Maintain model performance in production, troubleshoot issues, and roll out updates as needed.
- Research & Innovation: Keep current with advances in ML and CV, and apply new methods to solve business problems.
Your Profile:
- Master’s degree (or equivalent) in Computer Science, Machine Learning, or a related field.
- 3+ years of experience deploying ML models in production.
- Proficient in Python and ML frameworks (e.g., TensorFlow, PyTorch).
- Experience working with cloud platforms and containerised deployments (e.g., Docker, Kubernetes).
- Solid grounding in computer vision and experience with large-scale data.
- Bonus: exposure to reinforcement learning methods.
What’s on Offer:
- Flexible Work Setup: Hybrid-first approach with the option to work remotely or from our London collaboration space.
- Equity Options: Share in the company’s long-term success.
- Time Off: Up to 34 days annual leave including UK public holidays.
- Health & Wellbeing: Comprehensive private health cover (including mental health, dental, optics, and travel insurance).
- Retreats & Team Events: Regular in-person team gatherings and an annual company-wide retreat.
- Pension Scheme: Employer-supported contribution plan.
- Culture: Inclusive, open-minded, and team-oriented working environment.
Machine Learning Engineer – Computer Vision Focus employer: Brio Digital
Contact Detail:
Brio Digital Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer – Computer Vision Focus
✨Tip Number 1
Familiarise yourself with the latest advancements in computer vision and machine learning. Follow relevant blogs, attend webinars, and participate in online forums to stay updated. This knowledge will not only help you during interviews but also demonstrate your passion for the field.
✨Tip Number 2
Build a portfolio showcasing your projects related to computer vision. Include examples of model development, data handling, and algorithm tuning. Having tangible evidence of your skills can set you apart from other candidates and give you something concrete to discuss during interviews.
✨Tip Number 3
Network with professionals in the machine learning and computer vision community. Attend meetups, conferences, or online events to connect with others in the industry. These connections can lead to valuable insights and potential referrals for job opportunities.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and system design questions related to machine learning. Use platforms like LeetCode or HackerRank to sharpen your skills. Being well-prepared will boost your confidence and improve your chances of impressing the interviewers.
We think you need these skills to ace Machine Learning Engineer – Computer Vision Focus
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning and computer vision. Include specific projects where you've designed, trained, or deployed models, and mention any tools or frameworks you used, such as TensorFlow or PyTorch.
Craft a Compelling Cover Letter: In your cover letter, express your passion for machine learning and how it can drive real-world automation. Mention your experience with large datasets and cross-functional collaboration, and explain why you're excited about the opportunity to work with this technology company.
Showcase Your Projects: If applicable, include links to your GitHub or portfolio showcasing projects related to computer vision. Highlight any innovative solutions you've developed and the impact they had on performance or efficiency.
Prepare for Technical Questions: Anticipate technical questions related to model development, algorithm tuning, and data handling. Be ready to discuss your approach to optimising model performance and any challenges you've faced in previous roles.
How to prepare for a job interview at Brio Digital
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning frameworks like TensorFlow and PyTorch. Bring examples of projects where you've successfully deployed models, especially in computer vision tasks such as object detection or segmentation.
✨Demonstrate Collaboration Experience
Highlight instances where you've worked closely with cross-functional teams, including engineers and product managers. Discuss how you integrated ML solutions into applications and the impact it had on user experience.
✨Prepare for Problem-Solving Questions
Expect questions that assess your ability to troubleshoot and optimise model performance. Be ready to explain your approach to algorithm tuning and how you handle data quality issues in large datasets.
✨Stay Updated on Industry Trends
Research recent advancements in machine learning and computer vision. Be prepared to discuss how you can apply new methods to solve real-world business problems, showcasing your commitment to innovation.