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 systems and strong Python skills required.
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 a 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 employer: Groupe-Ebra-1
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 on cutting-edge projects, offering ample opportunities for professional growth and development. With competitive benefits including generous leave, fully funded healthcare, and equity options, we ensure our employees feel valued and empowered to contribute to our mission of enhancing human potential through robotics.
StudySmarter Expert Advice🤫
We think this is how you could land Neural Network Performance Engineer
✨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 bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions and practical scenarios. Practice explaining your past projects and how you tackled performance bottlenecks – clarity is key!
✨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 genuinely interested in joining our mission.
We think you need these skills to ace Neural Network Performance Engineer
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 at Humanoid!
Showcase Your Projects:Include links to any shipped models or publications you've contributed to. If you’ve got open-source code that demonstrates your ability to improve inference performance, flaunt it! This helps us see your practical experience in action.
Be Clear and Concise:When writing your cover letter, be clear about why you’re interested in this role and how your background fits. We appreciate crisp communication, so get straight to the point while showcasing your passion for robotics and AI.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, we love seeing candidates who take the initiative to connect directly with us.
How to prepare for a job interview at Groupe-Ebra-1
✨Know Your Neural Networks
Make sure you brush up on your knowledge of neural networks, especially the architectures and performance metrics relevant to the role. Be ready to discuss specific models you've worked with and how you tackled performance bottlenecks.
✨Showcase Your Coding Skills
Since strong Python skills are a must, prepare to demonstrate your coding abilities. Bring examples of your work, particularly any custom kernels or quantisation techniques you've implemented, and be ready to explain your thought process behind them.
✨Understand the Hardware
Familiarise yourself with the hardware mentioned in the job description, like NVIDIA Thor. Knowing how to optimise models for specific hardware can set you apart, so be prepared to discuss how you've done this in the past.
✨Communicate Clearly
Effective communication is key, especially when discussing trade-offs in model performance. Practice explaining complex concepts in simple terms, as this will help you convey your ideas clearly during the interview.
