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- Tasks: Optimise ML models for real-time use by thousands, design distributed training strategies, and develop performance tools.
- Company: Odyssey, an AI lab revolutionising industries with cutting-edge world models.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Other info: Enjoy autonomy in technical decisions and work with the latest GPU technology.
- Why this job: Join a pioneering team and make a significant impact in AI and machine learning.
- Qualifications: 8+ years in software engineering with a focus on ML performance and optimisation.
The predicted salary is between 80000 - 100000 £ 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, optimizing efficiency to minimize TFLOPS per user and training compute cost.
What you’ll do:
- Optimize 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 optimization, 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 optimization stacks.
- Highly metric‑based.
Member of Technical Staff, ML Performance employer: Odyssey
Contact Detail:
Odyssey Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Member of Technical Staff, ML Performance
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Odyssey or similar companies. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a project that highlights your experience with ML performance. This is your chance to demonstrate how you optimise models and tackle performance bottlenecks.
✨Tip Number 3
Ace the interview by being ready to discuss your past projects in detail. Be prepared to dive into your problem-solving process and how you've tackled challenges in distributed training and inference.
✨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, it shows you're genuinely interested in joining our team at Odyssey.
We think you need these skills to ace Member of Technical Staff, ML Performance
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 want to know what drives you!
Tailor Your Experience: Make sure to highlight your 8+ years of software engineering experience and any relevant work in ML performance. Use the job description as a guide to align your skills and experiences with what we’re looking for. This helps us see you as a perfect fit!
Be Metric-Based: Since we’re all about metrics, include quantifiable achievements in your application. Whether it’s reducing training time or improving model performance, numbers speak volumes. Show us how you’ve made an impact with data!
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 makes the whole process smoother for everyone involved.
How to prepare for a job interview at Odyssey
✨Know Your Stuff
Make sure you brush up on your knowledge of modern machine learning architectures and performance optimisation techniques. Be ready to discuss your experience with distributed training and inference, especially using tools like PyTorch or TensorFlow. This will show that you're not just familiar with the concepts but have hands-on experience.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled performance bottlenecks in previous projects. Think about times when you had to innovate or adapt quickly to solve a problem. This will demonstrate your problem-solving mindset and ability to think on your feet.
✨Get Technical
Be ready to dive deep into technical discussions. Familiarise yourself with the latest-generation GPUs and optimisation stacks. You might be asked to explain how you would design and implement distributed training strategies, so having a clear plan in mind can really set you apart.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's current projects and future goals. This shows your genuine interest in their work and helps you understand how you can contribute. Ask about their approach to scaling models for hundreds of thousands of users and what challenges they foresee in the near future.