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For Companies At a Glance
- Tasks: Design and build advanced machine learning systems for PCB design challenges.
- Company: DeepPCB is revolutionising PCB design with AI-powered tools for hardware teams globally.
- Benefits: Enjoy a flexible hybrid work policy and opportunities for growth and public speaking.
- Why this job: Join a diverse team tackling real-world AI challenges in a collaborative environment.
- Qualifications: BSc, MSc, or PhD in relevant fields with 5+ years of experience in machine learning.
- Other info: Represent DeepPCB at industry events and contribute to cutting-edge research.
The predicted salary is between 36000 - 60000 £ per year.
DeepPCB is My Client’s cutting-edge AI-powered Place & Route design tool for Printed Circuit Boards (PCBs). By combining deep reinforcement learning with high-performance computing, DeepPCB automates and accelerates PCB layout workflows—helping hardware teams around the world design faster, smarter, and more efficiently.
The Opportunity
We’re looking for a Research Engineer to join the DeepPCB team and push the boundaries of artificial intelligence in electronic design automation (EDA). In this role, you will design, build, and scale advanced machine learning systems—solving complex real-world PCB design challenges and contributing to the future of intelligent hardware engineering.
Key Responsibilities
- Research and implement scalable deep learning and reinforcement learning algorithms tailored for PCB place-and-route problems.
- Adapt ML algorithms for performance in distributed and GPU-accelerated environments.
- Build production-ready ML solutions and prototypes that directly integrate into the DeepPCB platform.
- Communicate research progress clearly to both technical and non-technical audiences.
- Collaborate across research, engineering, product, and business teams to drive innovation.
- Represent DeepPCB at industry conferences, client meetings, and research events as needed.
What We’re Looking For
- BSc, MSc, or PhD in Computer Science, Machine Learning, Electrical Engineering, or a related field.
- 5+ years of experience in applied machine learning, research engineering, or software development.
- Strong foundation in ML, deep learning, and preferably reinforcement learning.
- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Keras, or JAX.
- Experience with version control (GitHub, GitLab), clean code practices, and CI/CD workflows.
- Comfort working in a fast-paced, collaborative, and research-driven environment.
Nice to Haves
- Hands-on experience with PCB design, EDA software, or circuit-level optimization problems.
- Familiarity with high-performance computing tools like Kubernetes, Ray, or Dask.
- Open-source contributions, academic publications, or strong performance in ML competitions (e.g., Kaggle).
- Background in adjacent areas like Computer Vision, Representation Learning, or Simulation Environments.
Why Join My Client?
- Work on high-impact, real-world AI challenges that redefine hardware design.
- Join a diverse, mission-driven team combining cutting-edge research and product development.
- Flexible hybrid work policy with a collaborative office environment in London.
- Opportunities for growth, publication, and public speaking at top conferences and industry events.
Ready to Design the Future? Apply now to become part of a team that’s transforming electronic design with artificial intelligence.
Research Engineer employer: LinkedIn
Contact Detail:
LinkedIn Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Engineer
✨Tip Number 1
Familiarise yourself with the latest advancements in AI and machine learning, particularly in the context of PCB design. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the field by attending industry conferences or meetups related to AI and electronic design automation. Making connections can provide insights into the company culture and potentially lead to referrals.
✨Tip Number 3
Showcase your hands-on experience with relevant tools and frameworks like PyTorch or TensorFlow through personal projects or contributions to open-source initiatives. This practical knowledge can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss your previous research or projects in detail, especially those that involved deep learning or reinforcement learning. Being able to articulate your thought process and problem-solving skills will demonstrate your fit for the role.
We think you need these skills to ace Research Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, deep learning, and any specific projects related to PCB design. Use keywords from the job description to ensure it aligns with what DeepPCB is looking for.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and PCB design. Mention specific experiences that demonstrate your skills in research engineering and how they relate to the responsibilities outlined in the job description.
Showcase Your Projects: If you have worked on relevant projects, especially those involving ML frameworks like PyTorch or TensorFlow, include them in your application. Provide links to your GitHub or any publications to give evidence of your expertise.
Prepare for Technical Questions: Anticipate technical questions related to machine learning algorithms and PCB design during the interview process. Brush up on your knowledge of reinforcement learning and be ready to discuss your problem-solving approach.
How to prepare for a job interview at LinkedIn
✨Showcase Your Technical Skills
Make sure to highlight your experience with machine learning frameworks like PyTorch or TensorFlow. Be prepared to discuss specific projects where you've implemented deep learning algorithms, especially in the context of PCB design or similar fields.
✨Communicate Clearly
Since you'll need to convey complex ideas to both technical and non-technical audiences, practice explaining your research and projects in simple terms. This will demonstrate your ability to collaborate effectively across teams.
✨Demonstrate Problem-Solving Abilities
Prepare to discuss real-world challenges you've faced in previous roles and how you approached solving them. Highlight any experience with distributed systems or GPU-accelerated environments, as this is crucial for the role.
✨Engage with Their Mission
Research DeepPCB's goals and values before the interview. Show genuine interest in their mission to redefine hardware design with AI, and be ready to discuss how your background aligns with their objectives.
