At a Glance
- Tasks: Enhance neural network performance for cutting-edge humanoid robots in a dynamic environment.
- Company: Join Humanoid, a leader in robotics innovation and human potential amplification.
- Benefits: Enjoy 23 days annual leave, private healthcare, equity options, and free meals.
- Other info: Collaborate with top engineers and researchers while enjoying excellent career growth.
- Why this job: Be at the forefront of robotics and AI, making a real impact on technology.
- Qualifications: 3+ years in deep learning with strong Python and GPU architecture knowledge.
The predicted salary is between 60000 - 80000 € per year.
Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially‑scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 Alpha - our rapidly developed humanoid platform now running in real industrial pilots - and we’re growing the team to take it even further.
About the Role
We're hiring a Neural Network Performance Engineer to join our VLA team based in London. In this role, you will work on all aspects of running capable neural‑network based control policies at a high rate with minimal latency, both on cloud hardware and onboard. Your work will be critical to delivering smooth robot motions while reacting to environment changes as quickly as possible.
What You'll Do
- Analyze performance bottlenecks of a particular model architecture and come up with potential improvements.
- Make the model run on new hardware (e.g. NVIDIA Thor) efficiently.
- Implement custom kernels to reduce memory throughput requirements where it matters.
- Quantize a model with minimal loss of quality.
- Suggest and implement changes of model architecture that will enable better performance characteristics without sacrificing model capabilities.
What We're Looking For
- 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it.
- 1+ years experience working on performance of neural network inference (analyzing bottlenecks, writing custom kernels, quantizing models, fighting deep learning compilers).
- Excellent understanding of GPU architecture and why some models run faster than others.
- Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.
- You document experiments clearly and communicate trade‑offs crisply.
Nice to have:
- Robotics or autonomous driving experience.
- Open source code showcasing your ability to improve inference performance.
- Publications at ICLR/ICML/NeurIPS or equivalent open‑source contributions.
- Familiarity with vision‑language (VLM) or vision‑language‑action (VLA) models.
What We Offer
- Meaningful time off to rest and recharge: 23 days of annual leave (accrued), 15 days of paid sick leave, and paid company holidays.
- Fully funded private healthcare for UK employees, with broad provider access, virtual and in‑person care, and strong mental health and serious illness support.
- Equity included–we believe builders should share in what they build.
- Pension scheme with a total 8% contribution (5% employee, 3% employer) on full earnings.
- Free daily breakfast, catered lunch, and snacks in‑office.
- Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics.
- Freedom to influence the product and own key initiatives.
Neural Network Performance Engineer in London employer: Humanoid
At Humanoid, we are dedicated to pushing the boundaries of robotics and AI, making us an exceptional employer for those passionate about technology and innovation. Our London-based team thrives in a collaborative environment where engineers and researchers work together to create cutting-edge humanoid robots, offering ample opportunities for professional growth and meaningful contributions. With competitive benefits including generous leave, fully funded healthcare, and equity options, we ensure our employees are well-supported while they help shape the future of human-robot interaction.
StudySmarter Expert Advice🤫
We think this is how you could land Neural Network Performance Engineer in London
✨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 related to neural networks and performance optimisation. 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 deep learning and neural networks, and be ready to discuss your past projects in detail.
✨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 mission at Humanoid.
We think you need these skills to ace Neural Network Performance Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Neural Network Performance Engineer role. Highlight your experience with deep-learning systems and any relevant projects you've worked on. We want to see how your skills align with our mission!
Showcase Your Projects:Include links to any open-source contributions or publications you've made. This gives us a glimpse into your work and how you tackle performance issues in neural networks. Don’t be shy about showing off your achievements!
Be Clear and Concise:When writing your cover letter, keep it clear and to the point. Explain why you're excited about the role and how your background makes you a great fit. We appreciate straightforward communication that gets to the heart of the matter.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s the easiest way for us to track your application and ensure it reaches the right people. Plus, we love seeing candidates who take the initiative!
How to prepare for a job interview at Humanoid
✨Know Your Neural Networks
Make sure you brush up on your knowledge of neural networks, especially the architectures you've worked with. Be ready to discuss specific models you've built or optimised, and how you tackled performance bottlenecks. This will show that you not only understand the theory but can apply it practically.
✨Showcase Your Coding Skills
Since strong Python skills are a must, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot or discuss your previous projects. Bring examples of your code, especially any custom kernels or quantisation techniques you've implemented, to highlight your hands-on experience.
✨Communicate Clearly
Effective communication is key, especially when discussing trade-offs in model performance. Practice explaining complex concepts in simple terms, as you'll need to convey your ideas clearly to both technical and non-technical team members. This will help you stand out as a collaborative team player.
✨Research Humanoid's Work
Familiarise yourself with Humanoid's projects, particularly HMND-01 Alpha. Understanding their mission and how your role fits into their goals will not only impress your interviewers but also help you tailor your answers to align with their vision for the future of robotics.
