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For Companies At a Glance
- Tasks: Optimise AI/ML models and design GPU components for performance.
- Company: Join a cutting-edge tech company focused on AI innovation.
- Benefits: Competitive salary, flexible work options, and growth opportunities.
- Why this job: Make a real impact in AI by optimising large-scale models.
- Qualifications: Experience with deep learning, CUDA, and cloud platforms required.
- Other info: Collaborative environment with a focus on innovation and experimentation.
The predicted salary is between 36000 - 60000 £ per year.
Requirements
- Significant hands-on experience optimizing deep learning models
- Proven ability to profile and debug performance bottlenecks
- Experience with distributed or large-scale training and inference
- Familiarity with techniques such as mixed precision, quantization, distillation, pruning, caching, and batching
- Experience with large models (e.g., transformers)
- Practical CUDA development experience
- Deep understanding of at least one major deep learning framework (ideally PyTorch)
- Experience building and operating ML systems on cloud platforms (AWS, Azure, or GCP)
- Comfort working with experiment tracking, monitoring, and evaluation pipelines
Job Description
- Optimize and own performance of AI/ML foundation model, design GPU components, reduce latency, and work with founders on optimization goals.
- Own the performance, scalability, and reliability of the company's foundation model in both training and inference.
- Profile and optimize the end-to-end ML stack: data pipelines, training loops, inference serving, and deployment.
- Design and implement GPU-accelerated components, including custom CUDA kernels where off-the-shelf libraries are insufficient.
- Reduce latency and cost per inference token while maximizing throughput and hardware utilization.
- Work closely with the founders to translate product requirements into concrete optimization goals and technical roadmaps.
- Build internal tooling, benchmarks, and evaluation harnesses to help the team experiment, debug, and ship safely.
- Contribute to model architecture and system design where it impacts performance and robustness.
ML Engineer with Large Models Experience employer: Talent Search PRO
Contact Detail:
Talent Search PRO Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer with Large Models Experience
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with ML engineers 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 involving deep learning models and CUDA development. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of performance optimisation techniques and deep learning frameworks like PyTorch. Practise coding challenges and be ready to discuss your past experiences with large models.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented ML engineers. Tailor your application to highlight your experience with distributed training and inference, and let us know how you can help optimise our foundation model.
We think you need these skills to ace ML Engineer with Large Models Experience
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your hands-on experience with deep learning models and any specific projects you've worked on. We want to see how you've optimised performance and tackled challenges in the past!
Be Specific About Your Experience: When mentioning your familiarity with techniques like mixed precision or quantization, give us examples of how you've applied these in real-world scenarios. This helps us understand your practical knowledge better.
Tailor Your Application: Don’t just send a generic application! Tailor your CV and cover letter to reflect the requirements listed in the job description. We love seeing candidates who take the time to connect their experience with what we’re looking for.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands and shows us you’re serious about joining the StudySmarter team!
How to prepare for a job interview at Talent Search PRO
✨Know Your Models Inside Out
Make sure you can discuss your hands-on experience with deep learning models in detail. Be ready to explain how you've optimised performance, tackled bottlenecks, and the specific techniques you've used like quantization or pruning.
✨Show Off Your CUDA Skills
Since practical CUDA development is a must, brush up on your CUDA knowledge. Prepare to talk about any custom kernels you've designed and how they improved performance. Real-world examples will make your experience stand out!
✨Familiarise Yourself with Cloud Platforms
If you've worked with AWS, Azure, or GCP, be prepared to discuss your experience. Highlight any ML systems you've built and how you managed scalability and reliability in the cloud. This shows you're not just a coder but also a problem solver.
✨Prepare for Technical Deep Dives
Expect to dive deep into technical discussions about model architecture and system design. Think about how your past projects align with the company's optimisation goals and be ready to share insights on improving performance and robustness.
