Neural Network Performance Engineer

Thehumanoid; Greater London; On-site; GBP 65,000 - 85,000; Full time

Benefits offered by this job

23 days annual leave

Fully funded private healthcare

Pension scheme with 8% contribution

Free daily breakfast

Catered lunch and snacks

Job summary

Thehumanoid is seeking a talented Neural Network Performance Engineer to improve the performance of neural network-based control policies in London. This role involves enhancing model efficiency, analyzing performance bottlenecks, and implementing optimizations for neural networks.

The ideal candidate has significant experience in deep-learning systems, custom kernels, and GPU architecture, while contributing to innovative robot technology. Coming with competitive benefits including private healthcare and equity ownership, this position promises impactful contributions to AI research and robotics.

Qualifications

• 3+ years building deep-learning systems with shipped models.
• 1+ years experience in neural network inference performance.
• Strong understanding of GPU architecture.

Responsibilities

• Analyze performance bottlenecks of a model architecture.
• Make the model run efficiently on new hardware.
• Implement custom kernels to reduce memory throughput.

Skills

Deep-learning systems

Neural network performance analysis

GPU architecture understanding

Python

PyTorch

JAX

Job description

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.