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- Tasks: Lead the integration of machine learning in innovative electronics projects.
- Company: Join a cutting-edge team transforming industries with machine learning technology.
- Benefits: Enjoy private medical insurance, a generous pension scheme, and access to local clubs.
- Why this job: Be at the forefront of the machine learning revolution, working on diverse projects.
- Qualifications: Strong Python skills and a top STEM degree are essential; experience with ML frameworks is a plus.
- Other info: Full-time onsite role in a collaborative, low-management environment.
Spearheading the integration of machine learning into cutting-edge electronics. This innovative team of engineers and scientists are using machine learning tightly integrated with modern electronics to create new classes of products and radically alter the shape, performance and effectiveness of existing ones. As industry goes through a machine learning revolution you can be here, leading the charge.
You will work across the whole machine learning development lifecycle from initial concepts through data collection, cleaning and preparation to prototyping, testing and evaluation. With your models integrated with leading edge electronics, the final products are fully functional prototypes and demonstrator units manufactured at small scale. What’s special about this group is they do this dozens of times per year working across multiple domains. You can be working on computer vision for one project and generative models for the next.
Requirements:
- Strong knowledge of Python and its use in machine learning including hands-on experience building products with modern ML frameworks such as TensorFlow and PyTorch.
- Broad knowledge of machine learning techniques across multiple domains and the ability to transition into new domains quickly.
- A top degree in a STEM subject.
- UK national.
- While not required, experience deploying machine learning onto a range of hardware from resource constrained embedded systems through to edge computing is desirable.
- Any knowledge of GPU programming languages and frameworks (CUDA, ROCm, etc) is a plus.
Your future colleagues will be similarly highly skilled, with experience across industry and the drive to innovate. You will find yourself in a low-management work environment that encourages teamwork and respect for individuals' expertise. Benefits include private medical insurance, generous pension scheme and access to local social and sports clubs. Please note, you are required to be onsite full-time for this position.
Contact Detail:
ECM Selection (Holdings) Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with the latest trends in machine learning and electronics. Follow industry leaders on social media, read relevant blogs, and participate in online forums to stay updated. This knowledge will help you engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the field of machine learning and electronics. Attend meetups, webinars, or conferences where you can connect with potential colleagues or mentors. Building these relationships can lead to valuable insights and job referrals.
✨Tip Number 3
Showcase your hands-on experience with Python and ML frameworks like TensorFlow and PyTorch through personal projects or contributions to open-source. Having a portfolio that demonstrates your skills can set you apart from other candidates.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and machine learning problems. Use platforms like LeetCode or HackerRank to sharpen your skills. Being well-prepared will boost your confidence and performance during the interview process.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of a Machine Learning Engineer. Familiarise yourself with the technologies mentioned, such as Python, TensorFlow, and PyTorch, to tailor your application accordingly.
Highlight Relevant Experience: In your CV and cover letter, emphasise your hands-on experience with machine learning frameworks and any projects you've worked on that demonstrate your ability to transition between different domains. Be specific about your contributions and the outcomes.
Craft a Tailored Cover Letter: Write a compelling cover letter that connects your skills and experiences to the job description. Mention your enthusiasm for working in a low-management environment and your commitment to innovation in machine learning and electronics.
Proofread Your Application: Before submitting, carefully proofread your CV and cover letter for any errors or inconsistencies. Ensure that all information is accurate and that your application reflects your best self, as attention to detail is crucial in this field.
How to prepare for a job interview at ECM Selection (Holdings) Limited
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python and machine learning frameworks like TensorFlow and PyTorch. Bring examples of projects you've worked on, especially those that demonstrate your ability to build products using these technologies.
✨Demonstrate Adaptability
Since the role involves transitioning between different domains, highlight your ability to learn quickly and adapt to new challenges. Share specific instances where you successfully applied machine learning techniques in various contexts.
✨Discuss Real-World Applications
Talk about how you've integrated machine learning into practical applications, particularly in electronics. If you have experience with deploying models on hardware, be sure to mention it, as this is a desirable skill for the position.
✨Emphasise Teamwork and Collaboration
Given the low-management environment, it's important to convey your ability to work well in a team. Share experiences where you collaborated with others to achieve a common goal, showcasing your respect for individual expertise.
