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- Tasks: Architect and optimise AI training infrastructure for scalable systems.
- Company: Reflection, a leading UK tech company focused on AI innovation.
- Benefits: Top-tier salary, comprehensive health benefits, and a supportive work environment.
- Other info: Dynamic role with opportunities for professional growth in AI.
- Why this job: Join a cutting-edge team and shape the future of AI technology.
- Qualifications: Experience in distributed systems and tools like PyTorch and JAX.
The predicted salary is between 60000 - 80000 £ per year.
Reflection, based in the United Kingdom, is seeking a Software Engineer to architect and optimize the training infrastructure for AI models. The role focuses on building scalable systems for reinforcement learning and distributed training, requiring deep experience in distributed systems.
Candidates should have practical skills in tools like PyTorch and JAX, and a robust understanding of performance optimization.
The position offers top-tier compensation and comprehensive health benefits, alongside a supportive work environment.
Research Software Engineer - ML Training Infrastructure employer: Reflection
Contact Detail:
Reflection Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Software Engineer - ML Training Infrastructure
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, 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 involving PyTorch and JAX. 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 distributed systems and performance optimisation. Practice coding challenges and system design questions to boost your confidence and impress interviewers.
✨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 Research Software Engineer - ML Training Infrastructure
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with distributed systems and tools like PyTorch and JAX. 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 building scalable systems for AI models. We love seeing enthusiasm and a clear understanding of the role.
Showcase Your Problem-Solving Skills: In your application, include examples of how you've tackled performance optimization challenges in the past. We’re looking for candidates who can think critically and innovate in their approach.
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 with us at Reflection!
How to prepare for a job interview at Reflection
✨Know Your Tech Inside Out
Make sure you’re well-versed in the tools mentioned in the job description, like PyTorch and JAX. Brush up on your knowledge of distributed systems and performance optimisation techniques, as these will likely come up during technical questions.
✨Showcase Your Projects
Prepare to discuss any relevant projects you've worked on, especially those involving reinforcement learning or scalable systems. Be ready to explain your thought process, the challenges you faced, and how you overcame them.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the company’s current AI projects or their approach to training infrastructure. This shows your genuine interest and helps you gauge if the company is the right fit for you.
✨Practice Problem-Solving
Expect some coding challenges or problem-solving scenarios related to distributed training. Practice common algorithms and data structures, and be ready to think aloud during the interview to demonstrate your problem-solving approach.