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- Tasks: Optimise ML models for real-time use and design distributed training strategies.
- Company: Odyssey, an AI lab revolutionising various industries with cutting-edge technology.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Other info: Work with the latest GPUs and enjoy autonomy in technical decisions.
- Why this job: Join a pioneering team and make a significant impact on the future of AI.
- Qualifications: 8+ years in software engineering with a focus on ML performance and optimisation.
The predicted salary is between 80000 - 100000 ÂŁ per year.
Odyssey is an AI lab pioneering general‑purpose world models: causal, multimodal systems that learn to predict and interact with the world over long horizons, while generating real‑time, interactive simulations from any starting point. This foundational technology promises to revolutionise robotics, science, healthcare, education, gaming, defence, and beyond.
We’re seeking those who are obsessed with gaining every last drop of performance from complex systems. We’re building inference infrastructure to scale to hundreds of thousands of users within a year, while also working with massive, ever‑growing datasets and models in training. Your focus will be ensuring our models deliver exceptional speed, reliability, and scalability in both the training and inference phases, optimising efficiency to minimise TFLOPS per user and training compute cost.
What you’ll do:
- Optimise models that will be used in real‑time by hundreds of thousands of users.
- Design and implement distributed training strategies to reduce training time and resource consumption on large GPU clusters.
- Partner with our elite team of ML researchers and engineers to ensure model architectures are highly performant from conception.
- Develop sophisticated tools to identify performance bottlenecks and stability issues in both training and serving environments.
- Pioneer innovative approaches, frameworks, and system designs that enhance performance metrics across our model development and inference infrastructure.
- Have significant autonomy in technical decisions.
- Use the latest‑generation GPUs.
Who you are:
- 8+ years of software engineering experience, with significant work in ML performance.
- Deep insight into modern machine learning architectures with a natural instinct for performance optimisation, particularly distributed training and inference.
- Track record of owning projects end to end.
- Problem‑solving mindset with the ability to acquire new skills as needed.
- Proficiency with PyTorch (or TF/JAX) and Triton as well as NVIDIA GPU ecosystems and optimisation stacks.
- Highly metric‑based.
Member of Technical Staff, ML Performance in London employer: Odyssey
Contact Detail:
Odyssey Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Member of Technical Staff, ML Performance in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML performance. We want to see how you’ve optimised models and tackled challenges head-on.
✨Tip Number 3
Prepare for technical interviews by brushing up on your problem-solving skills. We recommend practicing coding challenges and discussing your thought process out loud to impress your interviewers.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Member of Technical Staff, ML Performance in London
Some tips for your application 🫡
Show Your Passion for Performance: When writing your application, let us see your obsession with performance optimisation shine through. Share specific examples of how you've maximised efficiency in past projects, especially in ML contexts. We love to see that drive!
Tailor Your Experience: Make sure to align your experience with the job description. Highlight your 8+ years in software engineering and any relevant work with ML performance. We want to know how your skills can help us scale our systems effectively.
Be Clear and Concise: Keep your application straightforward and to the point. Use clear language to describe your achievements and technical skills. We appreciate a well-structured application that makes it easy for us to see your qualifications.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at Odyssey!
How to prepare for a job interview at Odyssey
✨Know Your ML Stuff
Make sure you brush up on your knowledge of modern machine learning architectures. Be ready to discuss specific examples where you've optimised performance, especially in distributed training and inference. This will show that you’re not just familiar with the theory but have practical experience too.
✨Showcase Your Problem-Solving Skills
Prepare to share stories about how you've tackled complex problems in your previous roles. Think about times when you identified performance bottlenecks or stability issues and how you resolved them. This will demonstrate your problem-solving mindset and ability to think critically under pressure.
✨Familiarise Yourself with Tools and Frameworks
Since the role involves working with PyTorch, Triton, and NVIDIA GPU ecosystems, make sure you’re comfortable discussing these tools. If you’ve developed any sophisticated tools for performance optimisation, be ready to talk about them in detail. This shows you’re hands-on and can contribute from day one.
✨Understand the Company’s Vision
Take some time to research Odyssey and their mission in AI. Understanding their focus on real-time simulations and the impact of their technology across various sectors will help you align your answers with their goals. It also shows genuine interest, which is always a plus in interviews!