At a Glance
- Tasks: Create innovative embedded solutions using machine learning and vision systems.
- Company: 42 Technology Ltd, a leader in tech innovation and collaboration.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Exciting projects with potential for significant career advancement.
- Why this job: Join a cutting-edge team and shape the future of AI and vision technology.
- Qualifications: Experience in embedded systems, machine learning, and proficiency in Python, C, and C++.
The predicted salary is between 63000 - 77000 £ per year.
42 Technology Ltd is seeking an experienced embedded firmware/systems engineer with expertise in machine learning and machine vision. The role involves working directly with customers to translate requirements into deliverable embedded solutions, using Python, C and C++ with ML frameworks, and developing production-ready code. You will define software architecture, system requirements and use cases, collaborating with the wider team and reporting to the Head of Embedded Systems.
Embedded Machine Learning Engineer: AI & Vision Systems employer: The Learning Experience (old)
The Learning Experience (old) provides a collaborative work environment where employees can engage with innovative technology projects. The company emphasizes professional development and offers opportunities to work on cutting-edge solutions in machine learning and machine vision.
Contact Details:
The Learning Experience (old) Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Embedded Machine Learning Engineer: AI & Vision Systems
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Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at The Learning Experience (old) or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
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✨Tap into Online Developer Communities
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We think you need these skills to ace Embedded Machine Learning Engineer: AI & Vision Systems
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at The Learning Experience (old).
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at The Learning Experience (old) and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at The Learning Experience (old)
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If The Learning Experience (old) uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
