At a Glance
- Tasks: Optimise AI systems for high performance and scalability in real-world applications.
- Company: Join a cutting-edge tech company focused on AI, blockchain, and fintech.
- Benefits: Enjoy fully remote work, flexible hours, and a collaborative international team.
- Other info: Fast-paced culture that values creativity, autonomy, and continuous learning.
- Why this job: Make a real impact on innovative AI projects with global reach.
- Qualifications: Strong AI/ML engineering experience and passion for optimisation.
The predicted salary is between 60000 - 80000 £ per year.
This is an exciting opportunity for a highly technical AI engineer to contribute to the next generation of scalable and high-performance inference systems powering real-world AI applications. In this role, you will work on optimizing model serving architectures, improving latency and throughput, and enhancing deployment efficiency across cloud, edge, and resource-constrained environments. You will collaborate with globally distributed engineering and research teams focused on advanced AI systems, multi-modal architectures, and infrastructure innovation. The position offers a research-driven environment where experimentation, benchmarking, and performance optimization are central to daily work. Ideal candidates are passionate about low-level optimization, inference scalability, and building robust AI systems that deliver measurable production impact at scale.
Accountabilities
- Design, develop, and optimize advanced model serving architectures focused on high throughput, low latency, and efficient memory utilization.
- Build scalable inference pipelines capable of running across cloud, edge, and resource-constrained environments.
- Conduct controlled inference experiments in simulated and production environments to evaluate system performance and reliability.
- Monitor and analyze key performance metrics such as latency, throughput, memory consumption, token response time, and error rates.
- Develop and maintain benchmarking methodologies and performance validation frameworks for AI inference systems.
- Identify bottlenecks in serving pipelines, including batch processing inefficiencies, network overhead, and excessive memory usage.
- Optimize inference frameworks and deployment strategies for scalability, resilience, and operational efficiency.
- Collaborate with cross-functional engineering and research teams to integrate optimized inference solutions into production environments.
- Create high-quality testing datasets and deployment scenarios that reflect real-world operational challenges.
- Continuously improve inference infrastructure through experimentation, iteration, and adoption of cutting-edge AI serving techniques.
Requirements
- Strong experience in AI/ML engineering with a focus on inference optimization, model serving, or AI systems performance.
- Deep understanding of model deployment architectures and inference frameworks for large-scale AI applications.
- Expertise in optimizing latency, throughput, scalability, and memory footprint in production AI systems.
- Hands-on experience with performance monitoring, benchmarking, profiling, and bottleneck analysis.
- Strong knowledge of advanced AI model architectures, including multi-modal systems and resource-efficient models.
- Experience building and deploying AI systems across cloud, edge, or low-resource hardware environments.
- Proficiency in programming languages commonly used in AI infrastructure and optimization workflows.
- Strong analytical and problem-solving abilities with a research-oriented mindset.
- Ability to work independently in a highly distributed and fast-moving global environment.
- Excellent English communication skills and ability to collaborate across technical and non-technical teams.
- Passion for innovation, experimentation, and scalable AI infrastructure development.
Benefits
- Fully remote global work environment with flexible location options.
- Opportunity to work on cutting-edge AI, blockchain, and fintech technologies.
- Collaborative international team of highly skilled engineers and researchers.
- Exposure to innovative projects involving AI infrastructure, digital finance, and decentralized technologies.
- High-impact role with significant technical ownership and influence on product direction.
- Fast-paced and innovation-driven culture focused on experimentation and growth.
- Opportunities for continuous learning and professional development.
- Work environment that values autonomy, creativity, and technical excellence.
- Participation in projects with global reach and real-world scalability challenges.
AI Research Engineer (Kernel & Inference Optimization) employer: Jobgether
Join a forward-thinking company that champions innovation and technical excellence in the AI field. As an AI Research Engineer, you'll thrive in a fully remote global work environment that offers flexibility and the chance to collaborate with a diverse team of experts on cutting-edge projects. With a strong emphasis on continuous learning and professional development, this role provides significant ownership and influence over impactful AI solutions, making it an ideal place for passionate engineers eager to make a real-world difference.
StudySmarter Expert Advice🤫
We think this is how you could land AI Research Engineer (Kernel & Inference Optimization)
✨Tip Number 1
Network like a pro! Reach out to folks in the AI community, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to inference optimization and model serving. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common interview questions related to AI systems and be ready to discuss your past experiences in detail.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace AI Research Engineer (Kernel & Inference Optimization)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in AI/ML engineering, especially focusing on inference optimization and model serving. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
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 this role. Let us know what excites you about working on scalable inference systems.
Showcase Your Problem-Solving Skills:In your application, highlight specific examples where you've tackled performance issues or optimised AI systems. We love seeing how you approach challenges, so share those success stories that demonstrate your analytical mindset!
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’re considered for this exciting opportunity. Plus, it’s super easy!
How to prepare for a job interview at Jobgether
✨Know Your Stuff
Make sure you brush up on your knowledge of AI/ML engineering, especially around inference optimization and model serving. Be ready to discuss specific projects you've worked on, focusing on how you tackled latency and throughput challenges.
✨Showcase Your Problem-Solving Skills
Prepare to share examples of how you've identified and resolved bottlenecks in AI systems. Use the STAR method (Situation, Task, Action, Result) to structure your answers, highlighting your analytical approach and the impact of your solutions.
✨Familiarise Yourself with Their Tech Stack
Research the technologies and frameworks commonly used in the company’s AI infrastructure. If you know what programming languages they favour, be prepared to discuss your experience with them and how you’ve applied them in real-world scenarios.
✨Ask Insightful Questions
Prepare thoughtful questions about their current projects and challenges in AI infrastructure. This shows your genuine interest in the role and helps you gauge if the company aligns with your passion for innovation and experimentation.
