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- Tasks: Join a dynamic team to innovate and optimise ML models in robotics.
- Company: Be part of one of the UK's most exciting robotics start-ups.
- Benefits: Enjoy a full-time role with opportunities for growth and collaboration.
- Why this job: Make a real impact in sustainable marine tech while working with cutting-edge AI.
- Qualifications: Experience in ML model deployment and strong Python skills are essential.
- Other info: Work in an Agile environment and mentor fellow team members.
The predicted salary is between 36000 - 60000 £ per year.
Partnering with world class Data, AI, ML, Embedded Systems, & Robotics talent, one of the UK’s most exciting Robotics start-ups has partnered with us to bring in a Machine Learning Engineer into a distinguished team. As a key contributor, you will be responsible for innovation across the model lifecycle, from ideation & data preparation through to rapid experimentation to deployment and monitoring.
In addition to model development, ML Engineers here help optimize and enhance MLOps infrastructure and CI/CD pipelines. Staying at the forefront of advancements in ML and MLOps is a vital aspect of your work, as you integrate emerging techniques and tools to maximize impact on business objectives. You will make a significant impact by delivering efficient and accurate data processing and interpretation, enabling next-generation capabilities in autonomy and computer vision, and helping to establish leadership in sustainable marine and renewable technologies.
Responsibilities:
- Deploy and Manage ML Products – Maintain and optimize CI/CD pipelines, ensuring continuous integration and safe, reliable model releases.
- Monitor and Improve Model Performance – Proactively evaluate deployed models, identify issues, and implement enhancements.
- Drive Innovation and Experimentation – Stay updated with ML/AI advancements, prototype new concepts, and integrate emerging techniques.
- Hands-on ML Engineering – Develop model architectures, data pipelines, and leverage containerization/orchestration (e.g., Docker, Kubernetes).
- Ensure Code Quality and Best Practices – Conduct code reviews, advocate performance optimizations, and mentor team members.
- Collaborate in Agile Environments – Work cross-functionally, participate in Agile workflows, and refine processes for better ML solution delivery.
What I’m looking for:
- Proven experience designing, training, and deploying ML models in production environments.
- Passion for building scalable and reliable ML systems.
- Strong experience with MLOps practices and tooling (e.g., Docker, GitHub Actions, DVC/CML, CI/CD pipelines).
- Strong experience with ML for unstructured data, such as image, video, point cloud, sonar, radar, magnetometer, or seismic data.
- Technical expertise in AI: Deep Learning, Machine Learning, Statistics.
- Strong proficiency with Python (or similar), with a commitment to high-quality coding standards.
- Excellent awareness of software engineering and coding best practices.
Seniority level: Mid-Senior level
Employment type: Full-time
Job function: Information Technology
Industries: Information Services
Machine Learning Engineer - Robotics employer: European Tech Recruit
Contact Detail:
European Tech Recruit Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer - Robotics
✨Tip Number 1
Network with professionals in the robotics and machine learning fields. Attend industry meetups, webinars, or conferences to connect with potential colleagues and mentors who can provide insights into the company culture and expectations.
✨Tip Number 2
Showcase your hands-on experience with MLOps tools like Docker and CI/CD pipelines. Consider creating a portfolio of projects that demonstrate your ability to deploy and manage ML products effectively, as this will resonate well with the hiring team.
✨Tip Number 3
Stay updated on the latest advancements in machine learning and robotics. Follow relevant blogs, podcasts, and research papers to discuss these topics during interviews, showing your passion and commitment to continuous learning.
✨Tip Number 4
Prepare to discuss your experience with unstructured data, particularly in areas like image and video processing. Be ready to share specific examples of how you've tackled challenges in these domains, as this is crucial for the role.
We think you need these skills to ace Machine Learning Engineer - Robotics
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in designing, training, and deploying ML models. Emphasise your familiarity with MLOps practices and tools like Docker and CI/CD pipelines.
Craft a Compelling Cover Letter: In your cover letter, express your passion for building scalable ML systems and your commitment to high-quality coding standards. Mention specific projects or experiences that demonstrate your expertise in AI and machine learning.
Showcase Technical Skills: Clearly outline your technical skills related to the job description, such as proficiency in Python, deep learning, and handling unstructured data. Use examples to illustrate how you've applied these skills in previous roles.
Highlight Collaboration Experience: Since the role involves working in Agile environments, mention any past experiences where you collaborated cross-functionally. Discuss how you contributed to team success and refined processes for better ML solution delivery.
How to prepare for a job interview at European Tech Recruit
✨Showcase Your Technical Skills
Be prepared to discuss your experience with ML models, MLOps practices, and relevant tools like Docker and CI/CD pipelines. Bring examples of projects you've worked on that demonstrate your technical expertise and problem-solving abilities.
✨Demonstrate Your Passion for Innovation
Highlight your enthusiasm for staying updated with the latest advancements in machine learning and robotics. Share any personal projects or research that showcase your drive for innovation and experimentation in the field.
✨Prepare for Scenario-Based Questions
Expect questions that assess your ability to handle real-world challenges in ML engineering. Think about how you would approach model performance issues or optimising CI/CD pipelines, and be ready to articulate your thought process.
✨Emphasise Collaboration and Agile Experience
Since the role involves working in cross-functional teams, be sure to discuss your experience in Agile environments. Share examples of how you've collaborated with others to refine processes and deliver effective ML solutions.
