At a Glance
- Tasks: Join us to research and optimise cutting-edge AI systems and infrastructure.
- Company: Innovative tech firm focused on AI and distributed systems.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborate with experts and gain hands-on experience in a dynamic environment.
- Why this job: Make a real impact in the future of AI technology and systems.
- Qualifications: Degree in Computer Science or related field; strong knowledge of AI and distributed systems.
The predicted salary is between 36000 - 60000 € per year.
We are seeking Systems Research Engineers with a strong interest in computer systems, distributed AI infrastructure, and performance optimization. These roles are ideal for recent PhD graduates or exceptional BSc/MSc engineers looking to build research-driven engineering experience in areas such as operating systems, distributed systems, AI model serving, and machine learning infrastructure. You will work closely with senior architects on real-world projects, helping to prototype and optimize next-generation AI infrastructure.
Required Qualifications and Skills:
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field.
- Strong knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure.
- Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization.
- Proficiency in C/C++, with additional experience in Python for research prototyping.
- Solid grounding in systems research methodology, distributed algorithms, and profiling tools.
- Team-oriented mindset with effective technical communication skills.
Desired Qualifications and Experience:
- PhD in systems, distributed computing, or large-scale AI infrastructure.
- Publications in top-tier systems or ML conferences (NSDI, OSDI, EuroSys, SoCC, MLSys, NeurIPS, ICML, ICLR).
- Understanding of load balancing, state management, fault tolerance, and resource scheduling in large-scale AI inference clusters.
- Prior experience designing, deploying, and profiling high-performance cloud or AI infrastructure systems.
AI Systems Research Engineer employer: Microtech Global Ltd
As an AI Systems Research Engineer, you will join a dynamic and innovative team dedicated to pushing the boundaries of AI infrastructure. Our company fosters a collaborative work culture that values continuous learning and professional growth, offering mentorship from experienced architects and opportunities to engage in cutting-edge research projects. Located in a vibrant tech hub, we provide a stimulating environment where your contributions will directly impact the future of AI technology.
StudySmarter Expert Advice🤫
We think this is how you could land AI Systems Research Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI and systems research fields. Attend meetups, webinars, or conferences where you can chat with industry experts. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to distributed systems and AI infrastructure. This could be anything from GitHub repos to detailed case studies. It’s a great way to demonstrate your hands-on experience and passion for the field.
✨Tip Number 3
Prepare for those interviews! Brush up on your knowledge of operating systems and distributed algorithms. Practice explaining complex concepts in simple terms, as effective communication is key. We want to see how you think and solve problems on the spot!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Tailor your application to highlight your relevant experience and enthusiasm for AI systems research. Let’s get you on board!
We think you need these skills to ace AI Systems Research Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with distributed systems and AI infrastructure. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or research!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI systems and how your background makes you a perfect fit for our team. Let us know what excites you about the role!
Showcase Your Technical Skills:Be specific about your hands-on experience with LLM serving frameworks and programming languages like C/C++ and Python. We love seeing concrete examples of your work, so include any relevant projects or publications!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
How to prepare for a job interview at Microtech Global Ltd
✨Know Your Stuff
Make sure you brush up on your knowledge of distributed systems and AI infrastructure. Be ready to discuss specific frameworks like vLLM or Ray Serve, and how you've used them in your projects. This shows you're not just familiar with the theory but have practical experience too.
✨Showcase Your Projects
Prepare to talk about your past projects, especially those related to machine learning systems architecture or performance optimisation. Highlight any publications or presentations you've done at conferences, as this demonstrates your commitment to the field and your ability to communicate complex ideas effectively.
✨Ask Smart Questions
Interviews are a two-way street! Prepare insightful questions about the company's current projects or challenges they face in AI infrastructure. This not only shows your interest but also your understanding of the industry and the role you’re applying for.
✨Team Player Vibes
Since this role requires a team-oriented mindset, be ready to share examples of how you've collaborated with others in your previous roles. Discuss how you’ve communicated technical concepts to non-technical team members, as effective communication is key in a research-driven environment.
